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Badr S, Tahri M, Maanan M, Kašpar J, Yousfi N. An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach. Syst Biol Reprod Med 2025; 71:13-28. [PMID: 39873464 DOI: 10.1080/19396368.2024.2445831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 11/04/2024] [Accepted: 12/15/2024] [Indexed: 01/30/2025]
Abstract
Infertility has emerged as a significant public health concern, with assisted reproductive technology (ART) is a last-resort treatment option. However, ART's efficacy is limited by significant financial cost and physical discomfort. The aim of this study is to build Machine learning (ML) decision-support models to predict the optimal range of embryo numbers to transfer, using data from infertile couples identified through literature reviews. Binary classification models were developed to classify cases into two groups: those transferring two or fewer embryos and those transferring three or four. Four popular ML algorithms were used, including random forest (RF), logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN), considering seven criteria: the woman's age, sperm origin, the developmental qualities of four potential embryos, infertility duration, assessment of the woman, morphological qualities of the four best embryos on the day of transfer, and number of oocytes extracted. The stratified 3-fold cross-validation results show that the SVM model obtained the highest average accuracy (95.83%) and demonstrated the best overall performance, closely followed by the ANN and LR models with an average accuracy equal to 91.67%. The RF model achieved a slightly lower average accuracy (88.89%), which demonstrated the lowest variability. Testing on a new dataset revealed all models performed well, with ANN and SVM models classified all test set instances correctly, while the RF and LR models achieved 91.68% accuracy. These results highlight the superior generalization and effectiveness of the ANN and SVM models in guiding ART decisions.
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Affiliation(s)
- Sanaa Badr
- Department of Mathematics and Computer Science, Laboratory of Analysis, Modeling and Simulation, Faculty of Sciences Ben M'sik, Hassan II University of Casablanca, Casablanca, Morocco
| | - Meryem Tahri
- Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague (CZU), Praha-Suchdol, Czech Republic
| | - Mohamed Maanan
- Laboratory of Littoral, Environment, Remote Sensing and Geomatic (LETG) - UMR6554, Universit´e de Nantes, Nantes, France
| | - Jan Kašpar
- Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague (CZU), Praha-Suchdol, Czech Republic
| | - Noura Yousfi
- Department of Mathematics and Computer Science, Laboratory of Analysis, Modeling and Simulation, Faculty of Sciences Ben M'sik, Hassan II University of Casablanca, Casablanca, Morocco
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Wang H, Qin Y, Niu J, Chen H, Lu X, Wang R, Han J. Evolving perspectives on evaluating obesity: from traditional methods to cutting-edge techniques. Ann Med 2025; 57:2472856. [PMID: 40077889 PMCID: PMC11912248 DOI: 10.1080/07853890.2025.2472856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2024] [Revised: 02/09/2025] [Accepted: 02/12/2025] [Indexed: 03/14/2025] Open
Abstract
Objective: This review examines the evolution of obesity evaluation methods, from traditional anthropometric indices to advanced imaging techniques, focusing on their clinical utility, limitations, and potential for personalized assessment of visceral adiposity and associated metabolic risks. Methods: A comprehensive analysis of existing literature was conducted, encompassing anthropometric indices (BMI, WC, WHR, WHtR, NC), lipid-related metrics (LAP, VAI, CVAI, mBMI), and imaging technologies (3D scanning, BIA, ultrasound, DXA, CT, MRI). The study highlights the biological roles of white, brown, and beige adipocytes, emphasizing visceral adipose tissue (VAT) as a critical mediator of metabolic diseases. Conclusion: Although BMI and other anthropometric measurements are still included in the guidelines, indicators that incorporate lipid metabolism information can more accurately reflect the relationship between metabolic diseases and visceral obesity. At the same time, the use of more modern medical equipment, such as ultrasound, X-rays, and CT scans, allows for a more intuitive assessment of the extent of visceral obesity.
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Affiliation(s)
- Heyue Wang
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yaxin Qin
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jinzhu Niu
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Haowen Chen
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xinda Lu
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Rui Wang
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jianli Han
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
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3
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da Silva WM, Cazella SC, Rech RS. Deep learning algorithms to assist in imaging diagnosis in individuals with disc herniation or spondylolisthesis: A scoping review. Int J Med Inform 2025; 201:105933. [PMID: 40252304 DOI: 10.1016/j.ijmedinf.2025.105933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2025] [Revised: 04/13/2025] [Accepted: 04/16/2025] [Indexed: 04/21/2025]
Abstract
BACKGROUND Deep learning applications in medical imaging have advanced significantly, supporting the diagnosis of spinal disorders such as disc herniation and spondylolisthesis. This study aimed to review deep learning algorithms used in diagnostic imaging for these conditions. METHODS A scoping review was conducted following PRISMA-ScR guidelines and registered in the Open Science Framework. Literature searches were performed in PubMed, Lilacs, ScienceDirect, Web of Science, Wiley Online Library, Embase, IEEE Xplore, and Google Scholar. Studies published in the last ten years in English, Portuguese, or Spanish applying deep learning to lumbar spine imaging were included. Exclusions comprised reviews, expert opinions, and studies not focusing on lumbar imaging. Of 258 identified records, 71 duplicates were removed, leaving 187 for screening. After full-text assessment, 18 met eligibility criteria. RESULTS Nine studies investigated disc herniation, primarily using magnetic resonance imaging (MRI), while the remaining nine focused on spondylolisthesis based on X-ray imaging. Convolutional neural networks (CNNs), particularly ResNet-based architectures, were the most frequently used models, demonstrating high accuracy and sensitivity in classification tasks. MRI was predominant for disc herniation, while X-ray was preferred for spondylolisthesis. However, limitations included small dataset sizes, lack of external validation, and challenges in generalizing findings across populations. CONCLUSION While deep learning holds promise for enhancing diagnostic accuracy and efficiency, further research is needed to standardize evaluation methods, expand dataset diversity, and improve model robustness for real-world clinical applications.
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Affiliation(s)
- William Moraes da Silva
- Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Porto Alegre/RS, Brazil.
| | - Silvio César Cazella
- Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Porto Alegre/RS, Brazil.
| | - Rafaela Soares Rech
- Universidade Federal de Ciências da Saúde de Porto Alegre - UFCSPA, Porto Alegre/RS, Brazil.
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Lu J, Lu X, Wang Y, Zhang H, Han L, Zhu B, Wang B. Comparison between logistic regression and machine learning algorithms on prediction of noise-induced hearing loss and investigation of SNP loci. Sci Rep 2025; 15:15361. [PMID: 40316545 PMCID: PMC12048554 DOI: 10.1038/s41598-025-00050-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Accepted: 04/24/2025] [Indexed: 05/04/2025] Open
Abstract
To compare the comprehensive performance of conventional logistic regression (LR) and seven machine learning (ML) algorithms in Noise-Induced Hearing Loss (NIHL) prediction, and to investigate the single nucleotide polymorphism (SNP) loci significantly associated with the occurrence and progression of NIHL. A total of 1,338 noise-exposed workers from 52 enterprises in Jiangsu Province were included in this study. 88 SNP loci involving multiple genes related to noise exposure and hearing loss were detected. LR and multiple ML algorithms were employed to establish the NIHL prediction model with accuracy, recall, precision, F-score, R2 and AUC as performance indicators. Compared to conventional LR, the evaluated ML models Generalized Regression Neural Network (GRNN), Probabilistic Neural Network (PNN), Genetic Algorithm-Random Forests (GA-RF) demonstrate superior performance and were considered to be the optimal models for processing large-scale SNP loci dataset. The SNP loci screened by these models are pivotal in the process of NIHL prediction, which further improves the prediction accuracy of the model. These findings open new possibilities for accurate prediction of NIHL based on SNP locus screening in the future, and provide a more scientific basis for decision-making in occupational health management.
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Affiliation(s)
- Jie Lu
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast University, Nanjing, 210009, China
| | - Xinhao Lu
- School of Cyber Science and Engineering, Southeast University, Nanjing, 211189, China
- State Grid Jiangsu Electric Power Co., Ltd. Nanjing Power Supply Branch, Nanjing, 210012, China
| | - Yixiao Wang
- School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China
| | - Hengdong Zhang
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Prevention and Control (Jiangsu Academy of Preventive Medicine), No. 172 Jiangsu Road, Nanjing, 210000, Jiangsu, China
| | - Lei Han
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Prevention and Control (Jiangsu Academy of Preventive Medicine), No. 172 Jiangsu Road, Nanjing, 210000, Jiangsu, China
| | - Baoli Zhu
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast University, Nanjing, 210009, China.
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Prevention and Control (Jiangsu Academy of Preventive Medicine), No. 172 Jiangsu Road, Nanjing, 210000, Jiangsu, China.
- Jiangsu Preventive Medical Association, Nanjing, 210000, Jiangsu, China.
- Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
| | - Boshen Wang
- Key Laboratory of Environmental Medicine Engineering of Ministry of Education, Southeast University, Nanjing, 210009, China.
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Prevention and Control (Jiangsu Academy of Preventive Medicine), No. 172 Jiangsu Road, Nanjing, 210000, Jiangsu, China.
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Al Mohamad F, Donle L, Dorfner F, Romanescu L, Drechsler K, Wattjes MP, Nawabi J, Makowski MR, Häntze H, Adams L, Xu L, Busch F, Meddeb A, Bressem KK. Open-source Large Language Models can Generate Labels from Radiology Reports for Training Convolutional Neural Networks. Acad Radiol 2025; 32:2402-2410. [PMID: 39765434 DOI: 10.1016/j.acra.2024.12.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2024] [Revised: 12/13/2024] [Accepted: 12/13/2024] [Indexed: 04/23/2025]
Abstract
RATIONALE AND OBJECTIVES Training Convolutional Neural Networks (CNN) requires large datasets with labeled data, which can be very labor-intensive to prepare. Radiology reports contain a lot of potentially useful information for such tasks. However, they are often unstructured and cannot be directly used for training. The recent progress in large language models (LLMs) might introduce a new useful tool in interpreting radiology reports. This study aims to explore the use of the LLM to classify radiology reports and generate labels. These labels will be utilized then to train a CNN to detect ankle fractures to evaluate the effectiveness of using automatically generated labels. MATERIALS AND METHODS We used the open-weight LLM Mixtral-8×7B-Instruct-v0.1 to classify radiology reports of ankle x-ray images. The generated labels were used to train a CNN to recognize ankle fractures. The model's accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve were used for evaluation. RESULTS Using common prompt engineering techniques, a prompt was found that reached an accuracy of 92% on a test dataset. By parsing all radiology reports using the LLM, a training dataset of 15,896 images and labels was created. Using this dataset, a CNN was trained, which achieved an accuracy of 89.5% and an area under the receiver operating characteristic curve of 0.926 on a test dataset. CONCLUSION Our classification model based on labels generated with a large language model achieved high accuracy. This performance is comparable to models trained with manually labeled data, demonstrating the potential of language models in automating the labeling process. SUMMARY Large language models can be used to reliably detect pathologies in radiology reports. KEY RESULTS In this study, 7561 radiological reports of ankle X-ray images were automatically classified as describing an ankle fracture or not using a large language model. Using a dataset of 250 reports, the language model showed a classification accuracy of 92%. The generated labels were used to train an image classifier to detect ankle fractures on X-ray images. 15,896 images were used for training. The resulting model achieved an accuracy of 89.5% on a test dataset.
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Affiliation(s)
- Fares Al Mohamad
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (F.A.M., L.D., F.D., L.R., K.D., H.H., L.X., F.B.).
| | - Leonhard Donle
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (F.A.M., L.D., F.D., L.R., K.D., H.H., L.X., F.B.); Department of Obstetrics & Gynecology, University of Chicago, 5758 S Maryland Ave, Chicago, IL 60637 (L.D.)
| | - Felix Dorfner
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (F.A.M., L.D., F.D., L.R., K.D., H.H., L.X., F.B.); Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 149 13th St, Charlestown, MA 02129 (F.D.)
| | - Laura Romanescu
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (F.A.M., L.D., F.D., L.R., K.D., H.H., L.X., F.B.)
| | - Kristin Drechsler
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (F.A.M., L.D., F.D., L.R., K.D., H.H., L.X., F.B.)
| | - Mike P Wattjes
- Department of Neuroradiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (M.P.W., J.N., A.M.)
| | - Jawed Nawabi
- Department of Neuroradiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (M.P.W., J.N., A.M.)
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany (M.R.M., L.A.)
| | - Hartmut Häntze
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (F.A.M., L.D., F.D., L.R., K.D., H.H., L.X., F.B.)
| | - Lisa Adams
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University Munich, Ismaninger Str. 22, 81675 Munich, Germany (M.R.M., L.A.)
| | - Lina Xu
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (F.A.M., L.D., F.D., L.R., K.D., H.H., L.X., F.B.)
| | - Felix Busch
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (F.A.M., L.D., F.D., L.R., K.D., H.H., L.X., F.B.)
| | - Aymen Meddeb
- Department of Neuroradiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany (M.P.W., J.N., A.M.); Department of Neuroradiology, Hôpital Maison-Blanche, CHU Reims, Université Reims-Champagne-Ardenne, 45 Rue Cognacq-Jay, 51092 Reims, France (A.M.)
| | - Keno Kyrill Bressem
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Lazarettstraße 36, 80636 Munich, Germany (K.K.B.)
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6
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Meng F, Zhang T, Pan Y, Kan X, Xia Y, Xu M, Cai J, Liu F, Ge Y. A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images. BMC Med Imaging 2025; 25:142. [PMID: 40312690 PMCID: PMC12046700 DOI: 10.1186/s12880-025-01682-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2024] [Accepted: 04/18/2025] [Indexed: 05/03/2025] Open
Abstract
BACKGROUND The adrenal glands are small retroperitoneal organs, few reference standards exist for adrenal CT measurements in clinical practice. This study aims to develop a deep learning (DL) model for automated adrenal gland segmentation on non-contrast CT images, and to conduct a preliminary large-scale study on age-related volume changes in normal adrenal glands using the model output values. METHODS The model was trained and evaluated on a development dataset of annotated non-contrast CT scans of bilateral adrenal glands, utilizing nnU-Net for segmentation task. The ground truth was manually established by two experienced radiologists, and the model performance was assessed using the Dice similarity coefficient (DSC). Additionally, five radiologists provided annotations on a subset of 20 randomly selected cases to measure inter-observer variability. Following validation, the model was applied to a large-scale normal adrenal glands dataset to segment adrenal glands. RESULTS The DL model development dataset contained 1301 CT examinations. In the test set, the median DSC scores for the segmentation model of left and right adrenal glands were 0.899 and 0.904 respectively, and in the independent test set were 0.900 and 0.896. Inter-observer DSC for radiologist manual segmentation did not differ from automated machine segmentation (P = 0.541). The large-scale normal adrenal glands dataset contained 2000 CT examinations, the graph shows that adrenal gland volume increases first and then decreases with age. CONCLUSION The developed DL model demonstrates accurate adrenal gland segmentation, and enables a comprehensive study of age-related adrenal gland volume variations.
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Affiliation(s)
- Fanxing Meng
- Department of Radiology, Central China Subcenter of National Center for Cardiovascular Diseases, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, 450046, China
| | - Tuo Zhang
- Department of Radiology, Central China Subcenter of National Center for Cardiovascular Diseases, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, 450046, China
| | - Yukun Pan
- Department of Radiology, Central China Subcenter of National Center for Cardiovascular Diseases, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, 450046, China
| | - Xiaojing Kan
- Department of Radiology, Central China Subcenter of National Center for Cardiovascular Diseases, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, 450046, China
| | - Yuwei Xia
- Shanghai United Imaging Intelligence Co. Ltd, 701 Yunjin Road, Xuhui District, Shanghai, 200030, China
| | - Mengyuan Xu
- Department of Radiology, Central China Subcenter of National Center for Cardiovascular Diseases, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, 450046, China
| | - Jin Cai
- Department of Radiology, Central China Subcenter of National Center for Cardiovascular Diseases, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, 450046, China
| | - Fangbin Liu
- Department of Radiology, Central China Subcenter of National Center for Cardiovascular Diseases, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, 450046, China
| | - Yinghui Ge
- Department of Radiology, Central China Subcenter of National Center for Cardiovascular Diseases, Fuwai Central-China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, 450046, China.
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Chinta SV, Wang Z, Palikhe A, Zhang X, Kashif A, Smith MA, Liu J, Zhang W. AI-driven healthcare: Fairness in AI healthcare: A survey. PLOS DIGITAL HEALTH 2025; 4:e0000864. [PMID: 40392801 PMCID: PMC12091740 DOI: 10.1371/journal.pdig.0000864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/22/2025]
Abstract
Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. However, these advancements also introduce substantial ethical and fairness challenges, particularly related to biases in data and algorithms. These biases can lead to disparities in healthcare delivery, affecting diagnostic accuracy and treatment outcomes across different demographic groups. This review paper examines the integration of AI in healthcare, highlighting critical challenges related to bias and exploring strategies for mitigation. We emphasize the necessity of diverse datasets, fairness-aware algorithms, and regulatory frameworks to ensure equitable healthcare delivery. The paper concludes with recommendations for future research, advocating for interdisciplinary approaches, transparency in AI decision-making, and the development of innovative and inclusive AI applications.
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Affiliation(s)
| | - Zichong Wang
- Florida International University, Miami, Florida, United States of America
| | - Avash Palikhe
- Florida International University, Miami, Florida, United States of America
| | - Xingyu Zhang
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Ayesha Kashif
- Jose Marti MAST 6-12 Academy, Hialeah, Florida, United States of America
| | | | - Jun Liu
- Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Wenbin Zhang
- Florida International University, Miami, Florida, United States of America
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Elhanashi A, Saponara S, Zheng Q, Almutairi N, Singh Y, Kuanar S, Ali F, Unal O, Faghani S. AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction. J Imaging 2025; 11:141. [PMID: 40422999 DOI: 10.3390/jimaging11050141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2025] [Revised: 04/24/2025] [Accepted: 04/25/2025] [Indexed: 05/28/2025] Open
Abstract
Artificial intelligence (AI)-based object detection in radiology can assist in clinical diagnosis and treatment planning. This article examines the AI-based object detection models currently used in many imaging modalities, including X-ray Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US). The key models from the convolutional neural network (CNN) as well as the contemporary transformer and hybrid models are analyzed based on their ability to detect pathological features, such as tumors, lesions, and tissue abnormalities. In addition, this review offers a closer look at the strengths and weaknesses of these models in terms of accuracy, robustness, and speed in real clinical settings. The common issues related to these models, including limited data, annotation quality, and interpretability of AI decisions, are discussed in detail. Moreover, the need for strong applicable models across different populations and imaging modalities are addressed. The importance of privacy and ethics in general data use as well as safety and regulations for healthcare data are emphasized. The future potential of these models lies in their accessibility in low resource settings, usability in shared learning spaces while maintaining privacy, and improvement in diagnostic accuracy through multimodal learning. This review also highlights the importance of interdisciplinary collaboration among artificial intelligence researchers, radiologists, and policymakers. Such cooperation is essential to address current challenges and to fully realize the potential of AI-based object detection in radiology.
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Affiliation(s)
| | - Sergio Saponara
- Department of Information Engineering, University of Pisa, 56122 Pisa, Italy
| | - Qinghe Zheng
- School of Intelligence Engineering, Shandong Management University, Jinan 250100, China
| | - Nawal Almutairi
- Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 145111, Saudi Arabia
| | - Yashbir Singh
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Shiba Kuanar
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Farzana Ali
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Orhan Unal
- Departments of Radiology, School of Medicine, Medical Physics University of Wisconsin-Madison, Public Health Madison, Madison, WI 53705, USA
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9
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Cereser L, Borghesi A, De Martino M, Nadarevic T, Cicciò C, Agati G, Ciolli P, Collini V, Patruno V, Isola M, Imazio M, Zuiani C, Della Mea V, Girometti R. Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists. Eur J Radiol 2025; 185:111998. [PMID: 39983597 DOI: 10.1016/j.ejrad.2025.111998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2024] [Revised: 01/14/2025] [Accepted: 02/12/2025] [Indexed: 02/23/2025]
Abstract
PURPOSE Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory Society (ESC/ERS) guidelines. Chest contrast-enhanced computed tomography (CECT) is crucial in the non-invasive PH assessment. This study aimed to develop a machine learning (ML)-based educational resource for classifying PH cases via CECT according to ESC/ERS groups. METHODS We retrospectively included 172 PH patients who underwent CECT at two University Hospitals (Udine and Brescia). Three chest-devoted radiologists independently reviewed the CECTs, reporting on 13 features, including lung conditions, heart abnormalities, chronic thromboembolism, and mediastinal findings. Readers assigned the features as absent/present except for the left atrium (LA) anteroposterior diameter (measured in millimeters) and classified PH cases I-V with likelihood scores (1-100 %) for each group. The majority decisions for features and average LA diameter were used as ML inputs. The highest average likelihood scores determined group assignments, serving as ground truth. Various ML algorithms were tested using the Weka software and evaluated by accuracy, area under the ROC curve (AUROC), and F1-score. RESULTS After excluding three group V patients to avoid imbalance, the Naïve-Bayes algorithm showed 0.72 accuracy, 0.84 AUROC, and 0.72 F1-score. Accuracy values for group I-IV were 0.75, 0.78, 0.51, 0.79; AUROC values were 0.78, 0.84, 0.86, 0.87; F1-scores were 0.63, 0.79, 0.61, 0.84, respectively. CONCLUSIONS This study is the first to develop an ML-driven tool for classifying PH via chest CECT. While performance metrics require improvement, including the need for a larger sample size, the resource can potentially train non-dedicated radiologists in PH classification, supporting multidisciplinary reasoning.
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Affiliation(s)
- L Cereser
- Institute of Radiology, Department of Medicine (DMED), University of Udine, Italy; Institute of Radiology, University Hospital S. Maria della Misericordia, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy.
| | - A Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Italy.
| | - M De Martino
- Division of Medical Statistics, Department of Medicine (DMED), University of Udine, Italy.
| | - T Nadarevic
- Department of Diagnostic and Interventional Radiology, Clinical Hospital Center Rijeka, University of Rijeka, Croatia.
| | - C Cicciò
- Department of Diagnostic Imaging and Interventional Radiology, IRCCS Sacro Cuore Don Calabria Hospital, Negrar di Valpolicella (VR), Italy.
| | - G Agati
- Institute of Radiology, Department of Medicine (DMED), University of Udine, Italy.
| | - P Ciolli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Italy.
| | - V Collini
- Cardiology, Cardiothoracic Department, University Hospital S. Maria della Misericordia, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy.
| | - V Patruno
- Pulmonology Department, University Hospital S. Maria della Misericordia, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy.
| | - M Isola
- Division of Medical Statistics, Department of Medicine (DMED), University of Udine, Italy.
| | - M Imazio
- Cardiology, Cardiothoracic Department, University Hospital S. Maria della Misericordia, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy.
| | - C Zuiani
- Institute of Radiology, Department of Medicine (DMED), University of Udine, Italy; Institute of Radiology, University Hospital S. Maria della Misericordia, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy.
| | - V Della Mea
- Department of Mathematics, Computer Science, and Physics, University of Udine, Italy.
| | - R Girometti
- Institute of Radiology, Department of Medicine (DMED), University of Udine, Italy; Institute of Radiology, University Hospital S. Maria della Misericordia, Azienda Sanitaria Universitaria Friuli Centrale (ASUFC), Udine, Italy.
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10
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Su R, Tao X, Yan L, Liu Y, Chen CC, Li P, Li J, Miao J, Liu F, Kuai W, Hou J, Liu M, Mi Y, Xu L. Early screening, diagnosis and recurrence monitoring of hepatocellular carcinoma in patients with chronic hepatitis B based on serum N-glycomics analysis: A cohort study. Hepatology 2025:01515467-990000000-01210. [PMID: 40117651 DOI: 10.1097/hep.0000000000001316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Accepted: 03/02/2025] [Indexed: 03/23/2025]
Abstract
BACKGROUND AND AIMS HCC poses a significant global health burden, with HBV being the predominant etiology in China. However, current diagnostic markers lack the requisite sensitivity and specificity. This study aims to develop and validate serum N-glycomics-based models for the diagnosis and prognosis of HCC in patients with chronic hepatitis B-related cirrhosis. APPROACH AND RESULTS This study enrolled a total of 397 patients with chronic hepatitis B-related cirrhosis and HCC for clinical management. N-glycomics profiling was conducted on all participants, and clinical data were collected. First, machine learning-based models, Hepatocellular Carcinoma Glycomics Random Forest model and Hepatocellular Carcinoma Glycomics Support Vector Machine model, were established for early screening and diagnosis of HCC using N-glycomics. The AUC values in the validation set were 0.967 (95% CI: 0.930-1.000) and 0.908 (0.840-0.976) for Hepatocellular Carcinoma Glycomics Random Forest model and Hepatocellular Carcinoma Glycomics Support Vector Machine model, respectively, outperforming AFP (0.687 [0.575-0.765]) and Protein Induced by Vitamin K Absence or Antagonist-II (PIVKA-II) (0.665 [0.507-0.823]). It also showed superiority in subgroup analysis and external validation. Calibration and decision curve analysis also showed good predictive performance. Additionally, we developed a prognostic model, the prog-G model, based on N-glycans to monitor recurrence in patients with HCC after curative treatment. During the follow-up period, it was observed that this model correlated with the clinical condition of the patients and could identify all recurrent HCC cases (n=12) prior to imaging findings, outperforming AFP (n=7) and PIVKA-II (n=9), while also detecting recurrent lesions earlier than imaging. CONCLUSIONS N-glycomics models can effectively predict the occurrence and recurrence of HCC to improving the efficiency of clinical decision-making and promoting the precision treatment of HCC.
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Affiliation(s)
- Rui Su
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Tianjin Institute of Hepatology, Tianjin Second People's Hospital, Tianjin, China
- Department of Hepatology, Tianjin Integrated Traditional Chinese and Western Medicine Institute of Infectious Diseases, Tianjin, China
- Department of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Xuemei Tao
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Laboratory of Infectious and Liver Diseases, Center of Infectious Diseases, West China Hospital of Sichuan University, Chengdu, China
| | - Lihua Yan
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Tianjin Institute of Hepatology, Tianjin Second People's Hospital, Tianjin, China
- Department of Hepatology, Tianjin Integrated Traditional Chinese and Western Medicine Institute of Infectious Diseases, Tianjin, China
| | - Yonggang Liu
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Department of Pathology, Tianjin Second People's Hospital, Tianjin, China
| | - Cuiying Chitty Chen
- Department of Research and Development, Sysdiagno (Nanjing) Biotech Co. Ltd, Nanjing, Jiangsu Province, China
| | - Ping Li
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Department of Integrated Traditional Chinese and Western Medicine, Tianjin Second People's Hospital, Tianjin, China
| | - Jia Li
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Department of Hepatology, Tianjin Second People's Hospital, Tianjin, China
| | - Jing Miao
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Department of Traditional Chinese Medicine, Tianjin Second People's Hospital, Tianjin, China
| | - Feng Liu
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Department of Hepatology, Tianjin Second People's Hospital, Tianjin, China
| | - Wentao Kuai
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Department of Hepatology & Oncology, Tianjin Second People's Hospital, Tianjin, China
| | - Jiancun Hou
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Department of Surgery, Tianjin Second People's Hospital, Tianjin, China
| | - Mei Liu
- Department of Oncology, Beijing You'an Hospital, Capital Medical University, Beijing, China
| | - Yuqiang Mi
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Tianjin Institute of Hepatology, Tianjin Second People's Hospital, Tianjin, China
- Department of Hepatology, Tianjin Integrated Traditional Chinese and Western Medicine Institute of Infectious Diseases, Tianjin, China
- Department of Integrated Traditional Chinese and Western Medicine, Tianjin Second People's Hospital, Tianjin, China
| | - Liang Xu
- Clinical School of the Second People' s Hospital, Tianjin Medical University, Tianjin, China
- Tianjin Institute of Hepatology, Tianjin Second People's Hospital, Tianjin, China
- Department of Hepatology, Tianjin Integrated Traditional Chinese and Western Medicine Institute of Infectious Diseases, Tianjin, China
- Department of Hepatology & Oncology, Tianjin Second People's Hospital, Tianjin, China
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11
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Alguizzani N, Khouqeer F, Alorini R, Oberi I. Perceptions of Cardiac Surgeons Regarding the Integration of Artificial Intelligence in Cardiac Surgery. J Saudi Heart Assoc 2025; 37:1. [PMID: 40134414 PMCID: PMC11932695 DOI: 10.37616/2212-5043.1424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2024] [Revised: 01/29/2025] [Accepted: 02/13/2025] [Indexed: 03/27/2025] Open
Abstract
Background After the surge of artificial intelligence in late 2022, researchers started exploring the idea of using artificial intelligence in the medical field. Considering the endless possibilities of artificial intelligence, there is still some hesitation toward its use in the medical field. This study aims to explore the attitudes of cardiac surgeons toward involving artificial intelligence in diagnosing cardiac conditions and planning cardiac operations. Methodology This study surveyed cardiac surgeons on AI integration in their field using a cross-sectional design and purposive sampling. Data were collected via a structured questionnaire and analyzed in IBM SPSS 29.0. Results Our study included 33 cardiac surgeons primarily male (n =26, 78.8 %) and Saudi nationals (n =26, 78.8 %), assessed attitudes towards AI in cardiac surgery. A significant majority supported AI for pre-operative (n =17, 51.5 %), intra-operative (n =11, 33.3 %), and post-operative tasks (n =13, 39.4 %). The overall positive attitude towards AI was 54.2 % and overall positive perception towards AI was 50 %. However, perceptions of AI's integration into healthcare varied, with the highest approval for Documentation AI Assistance (n =13, 39.40 %). No significant demographic differences were found affecting attitudes towards AI (p-values ranging from 0.576 to 1.000). Conclusion Our study reveals a positive yet cautious attitude towards AI in cardiac surgery, recognizing its potential to improve precision and efficiency but emphasizing the irreplaceable need for human judgment and expertise in managing patient-specific variables.
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Affiliation(s)
- Nada Alguizzani
- King Faisal University, College of Medicine, Department of Surgery, Al Ahsaa,
Saudi Arabia
| | - Fareed Khouqeer
- King Faisal Specialist Hospital and Research Centre, Department of Cardiac Surgery, Riyadh,
Saudi Arabia
| | - Rasha Alorini
- Unaizah College of Medicine and Medical Sciences, Unaizah,
Saudi Arabia
| | - Imtenan Oberi
- College of Medicine, Jazan University, Jazan,
Saudi Arabia
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12
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Aldieri A, Gamage TPB, Amedeo La Mattina A, Loewe A, Pappalardo F, Viceconti M. Consensus statement on the credibility assessment of machine learning predictors. Brief Bioinform 2025; 26:bbaf100. [PMID: 40062621 PMCID: PMC11891646 DOI: 10.1093/bib/bbaf100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2024] [Revised: 01/13/2025] [Accepted: 02/22/2025] [Indexed: 05/13/2025] Open
Abstract
The rapid integration of machine learning (ML) predictors into in silico medicine has revolutionized the estimation of quantities of interest that are otherwise challenging to measure directly. However, the credibility of these predictors is critical, especially when they inform high-stakes healthcare decisions. This position paper presents a consensus statement developed by experts within the In Silico World Community of Practice. We outline 12 key statements forming the theoretical foundation for evaluating the credibility of ML predictors, emphasizing the necessity of causal knowledge, rigorous error quantification, and robustness to biases. By comparing ML predictors with biophysical models, we highlight unique challenges associated with implicit causal knowledge and propose strategies to ensure reliability and applicability. Our recommendations aim to guide researchers, developers, and regulators in the rigorous assessment and deployment of ML predictors in clinical and biomedical contexts.
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Affiliation(s)
- Alessandra Aldieri
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24 - 10129 Torino, Italy
| | | | - Antonino Amedeo La Mattina
- Medical Technology Laboratory, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano, 1/10 - 40136 Bologna, Italy
- Yi Li National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, 512 Huashan Rd, Jing'An, 200031, Shanghai, China
| | - Axel Loewe
- Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Francesco Pappalardo
- Department of Drug and Health Sciences, University of Catania, V.le A. Doria, 6, 95125 Catania, Italy
| | - Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum—University of Bologna, Via Zamboni, 33 - 40126 Bologna, Italy
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13
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Nader R, Autrusseau F, L'Allinec V, Bourcier R. Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario. IEEE TRANSACTIONS ON MEDICAL IMAGING 2025; 44:1347-1358. [PMID: 39504285 DOI: 10.1109/tmi.2024.3492313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2024]
Abstract
We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree, including the cerebral arteries, bifurcations and intracranial aneurysms. This model intends to provide a substantial dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms. The cerebral aneurysms most often occur on a particular structure of the vascular tree named the Circle of Willis. Various studies have been conducted to detect and monitor the aneurysms and those based on Deep Learning achieve the best performance. Specifically, in this work, we propose a full synthetic 3D model able to mimic the brain vasculature as acquired by Magnetic Resonance Angiography, Time Of Flight principle. Among the various MRI modalities, this latter allows for a good rendering of the blood vessels and is non-invasive. Our model has been designed to simultaneously mimic the arteries' geometry, the aneurysm shape, and the background noise. The vascular tree geometry is modeled thanks to an interpolation with 3D Spline functions, and the statistical properties of the background noise is collected from angiography acquisitions and reproduced within the model. In this work, we thoroughly describe the synthetic vasculature model, we build up a neural network designed for aneurysm segmentation and detection, finally, we carry out an in-depth evaluation of the performance gap gained thanks to the synthetic model data augmentation.
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14
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Yangi K, On TJ, Xu Y, Gholami AS, Hong J, Reed AG, Puppalla P, Chen J, Tangsrivimol JA, Li B, Santello M, Lawton MT, Preul MC. Artificial intelligence integration in surgery through hand and instrument tracking: a systematic literature review. Front Surg 2025; 12:1528362. [PMID: 40078701 PMCID: PMC11897506 DOI: 10.3389/fsurg.2025.1528362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2024] [Accepted: 01/31/2025] [Indexed: 03/14/2025] Open
Abstract
Objective This systematic literature review of the integration of artificial intelligence (AI) applications in surgical practice through hand and instrument tracking provides an overview of recent advancements and analyzes current literature on the intersection of surgery with AI. Distinct AI algorithms and specific applications in surgical practice are also examined. Methods An advanced search using medical subject heading terms was conducted in Medline (via PubMed), SCOPUS, and Embase databases for articles published in English. A strict selection process was performed, adhering to PRISMA guidelines. Results A total of 225 articles were retrieved. After screening, 77 met inclusion criteria and were included in the review. Use of AI algorithms in surgical practice was uncommon during 2013-2017 but has gained significant popularity since 2018. Deep learning algorithms (n = 62) are increasingly preferred over traditional machine learning algorithms (n = 15). These technologies are used in surgical fields such as general surgery (n = 19), neurosurgery (n = 10), and ophthalmology (n = 9). The most common functional sensors and systems used were prerecorded videos (n = 29), cameras (n = 21), and image datasets (n = 7). The most common applications included laparoscopic (n = 13), robotic-assisted (n = 13), basic (n = 12), and endoscopic (n = 8) surgical skills training, as well as surgical simulation training (n = 8). Conclusion AI technologies can be tailored to address distinct needs in surgical education and patient care. The use of AI in hand and instrument tracking improves surgical outcomes by optimizing surgical skills training. It is essential to acknowledge the current technical and social limitations of AI and work toward filling those gaps in future studies.
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Affiliation(s)
- Kivanc Yangi
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
| | - Thomas J. On
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
| | - Yuan Xu
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
| | - Arianna S. Gholami
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
| | - Jinpyo Hong
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
| | - Alexander G. Reed
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
| | - Pravarakhya Puppalla
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
| | - Jiuxu Chen
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
| | - Jonathan A. Tangsrivimol
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
| | - Baoxin Li
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
| | - Marco Santello
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Michael T. Lawton
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
| | - Mark C. Preul
- The Loyal and Edith Davis Neurosurgical Research Laboratory, Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, AZ, United States
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15
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Alimiri Dehbaghi H, Khoshgard K, Sharini H, Khairabadi SJ. Diagnosis of traumatic liver injury on computed tomography using machine learning algorithms and radiomics features: The role of artificial intelligence for rapid diagnosis in emergency rooms. JOURNAL OF RESEARCH IN MEDICAL SCIENCES : THE OFFICIAL JOURNAL OF ISFAHAN UNIVERSITY OF MEDICAL SCIENCES 2024; 29:77. [PMID: 39871872 PMCID: PMC11771820 DOI: 10.4103/jrms.jrms_847_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 06/11/2024] [Accepted: 07/02/2024] [Indexed: 01/29/2025]
Abstract
Background The initial assessment of trauma is a time-consuming and challenging task. The purpose of this research is to examine the diagnostic effectiveness and usefulness of machine learning models paired with radiomics features to identify blunt traumatic liver injury in abdominal computed tomography (CT) images. Materials and Methods In this study, 600 CT scan images of people with mild and severe liver damage due to trauma and healthy people were collected from the Kaggle dataset. The axial images were segmented by an experienced radiologist, and radiomics features were extracted from each region of interest. Initially, 30 machine learning models were implemented, and finally, three machine learning models were selected including Light Gradient-Boosting Machine (LGBM), Ridge Classifier, and Extreme Gradient Boosting (XGBoost), and their performance was examined in more detail. Results The two criteria of precision and specificity of LGBM and XGBoost models in diagnosing mild liver injury were calculated to be 100%. Only 6.00% of cases were misdiagnosed by the LGBM model. The LGBM model achieved 100% sensitivity and 99.00% accuracy in diagnosing severe liver injury. The area under the receiver operating characteristic curve value and precision of this model were also calculated to be 99.00% and 98.00%, respectively. Conclusion The artificial intelligence models used in this study have great potential to improve patient care by assisting radiologists and other physicians in diagnosing and staging trauma-related liver injuries. These models can help prioritize positive studies, allow more rapid evaluation, and identify more severe injuries that may require immediate intervention.
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Affiliation(s)
- Hanieh Alimiri Dehbaghi
- Department of Medical Physics, Student Research Committee, University of Medical Sciences, Kermanshah, Iran
| | - Karim Khoshgard
- Department of Medical Physics, University of Medical Sciences, Kermanshah, Iran
| | - Hamid Sharini
- Department of Biomedical Engineering, University of Medical Sciences, Kermanshah, Iran
| | - Samira Jafari Khairabadi
- Department of Biostatistics, Student Research Committee, University of Medical Sciences, Kermanshah, Iran
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16
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Alimiri Dehbaghi H, Khoshgard K, Sharini H, Jafari Khairabadi S, Naleini F. Radiomics-based machine learning for automated detection of Pneumothorax in CT scans. PLoS One 2024; 19:e0314988. [PMID: 39652609 PMCID: PMC11627382 DOI: 10.1371/journal.pone.0314988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 11/18/2024] [Indexed: 12/12/2024] Open
Abstract
The increasing complexity of diagnostic imaging often leads to misinterpretations and diagnostic errors, particularly in critical conditions such as pneumothorax. This study addresses the pressing need for improved diagnostic accuracy in CT scans by developing an intelligent model that leverages radiomics features and machine learning techniques. By enhancing the detection of pneumothorax, this research aims to mitigate diagnostic errors and accelerate the process of image interpretation, ultimately improving patient outcomes. Data used in this study was extracted from the medical records of 175 patients with suspected pneumothorax. The collected images were preprocessed in Matlab software. Radiomics features were extracted from each image and finally, the machine learning models were implemented on these features. The used machine learning algorithms are Gradient Tree Boosting (GBM), eXtreme Gradient Boosting (XGBoost), and Light GBM. To evaluate the performance of models, various evaluation criteria such as precision, accuracy, specificity, sensitivity, F1 score, Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), and misclassification were calculated. According to the calculated evaluation criteria, in terms of accuracy, the Gradient Boosting Machine (GBM) model achieved the highest performance with an accuracy of 98.97%, followed closely by the XGBoost model at 98.29%. For precision, the GBM model outperformed the other models, recording a precision value of 99.55%. Regarding sensitivity, all three models-GBM, XGBoost, and LightGBM (LGBM)-demonstrated strong performance, with sensitivity values of 99%, 99%, and 100%, respectively, indicating minimal variation among them. The artificial intelligence models used in this study have significant potential to enhance patient care by supporting radiologists and other clinicians in the diagnosis of pneumothorax. These models can facilitate the prioritization of positive cases, expedite evaluations, and ultimately improve patient outcomes.
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Affiliation(s)
| | - Karim Khoshgard
- Department of Medical Physics, University of Medical Sciences, Kermanshah, Iran
| | - Hamid Sharini
- Department of Biomedical Engineering, University of Medical Sciences, Kermanshah, Iran
| | | | - Farhad Naleini
- Clinical Research Development Center, Imam Reza Hospital, University of Medical Sciences, Kermanshah, Iran
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17
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Urgel-Pinto R, Alcázar-Vara LA. Microstructural analysis applied to carbonate matrix acidizing: An overview and a case study. Micron 2024; 187:103724. [PMID: 39353266 DOI: 10.1016/j.micron.2024.103724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 09/12/2024] [Accepted: 09/20/2024] [Indexed: 10/04/2024]
Abstract
Carbonate formations constitute the primary source of hydrocarbon reservoirs worldwide, highlighting the importance of gaining a deeper understanding of matrix acidizing phenomena to enhance well productivity in these formations. Microstructural analysis of acidified cores is a key method to assess acidizing systems in carbonate rock samples. X-ray computed microtomography (Micro-CT) has significantly contributed to this field, primarily focusing on the qualitative and quantitative characterization of the profiles of carbonate mineral dissolution, also known as wormholes, created during matrix acidizing. This non-destructive evaluation technique allows the internal analysis of a sample without altering its internal structure or chemical composition. This work focuses on a comprehensive review of the research studies and the main applications of micro-CT for evaluating and characterizing matrix acidizing in carbonates. First, an overview of the fundamentals of micro-CT and acid stimulation in carbonates is provided. Next, the applications of micro-CT are discussed and grouped into microstructural characteristics of wormholes, dynamic in-situ experiments, and pore-scale modeling topics. A case study is presented with a methodology based on micro-CT analysis for matrix acidizing. Finally, future work and current challenges, such as 4D CT and CT-based acidizing models, are discussed.
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Affiliation(s)
- Roger Urgel-Pinto
- Postgraduate Academic Program, Instituto Mexicano del Petróleo, Veracruz, Mexico
| | - Luis A Alcázar-Vara
- Laboratory of Drilling, Completion Fluids and Cementing of Wells, Center of Technologies for Exploration and Production (CTEP), Instituto Mexicano del Petróleo, Veracruz, Mexico.
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18
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Öztürk EMA, Ünsal G, Erişir F, Orhan K. Prediction of bone invasion of oral squamous cell carcinoma using a magnetic resonance imaging-based machine learning model. Eur Arch Otorhinolaryngol 2024; 281:6585-6597. [PMID: 39083062 PMCID: PMC11564286 DOI: 10.1007/s00405-024-08862-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Accepted: 07/20/2024] [Indexed: 11/16/2024]
Abstract
OBJECTIVES Radiomics, a recently developed image-processing technology, holds potential in medical diagnostics. This study aimed to propose a machine-learning (ML) model and evaluate its effectiveness in detecting oral squamous cell carcinoma (OSCC) and predicting bone metastasis using magnetic resonance imaging (MRI). MATERIALS-METHODS MRI radiomic features were extracted and analyzed to identify malignant lesions. A total of 86 patients (44 with benign lesions without bone invasion and 42 with malignant lesions with bone invasion) were included. Data and clinical information were managed using the RadCloud Platform (Huiying Medical Technology Co., Ltd., Beijing, China). The study employed a hand-crafted radiomics model, with the dataset randomly split into training and validation sets in an 8:2 ratio using 815 random seeds. RESULTS The results revealed that the ML method support vector machine (SVM) performed best for detecting bone invasion (AUC = 0.999) in the test set. Radiomics tumor features derived from MRI are useful to predicting bone invasion from oral squamous cell carcinoma with high accuracy. CONCLUSIONS This study introduces an ML model utilizing SVM and radiomics to predict bone invasion in OSCC. Despite the promising results, the small sample size necessitates larger multicenter studies to validate and expand these findings.
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Affiliation(s)
- Elif Meltem Aslan Öztürk
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Lokman Hekim University, Ankara, Turkey.
| | - Gürkan Ünsal
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Ferhat Erişir
- Department of Otorhinolaryngology and Head and Neck Surgery, Faculty of Medicine, Near East University, Kyrenia, Cyprus
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
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Shao X, Pham A, Kuo TT. WebQuorumChain: A web framework for quorum-based health care model learning. INFORMATICS IN MEDICINE UNLOCKED 2024; 50:101590. [PMID: 39483487 PMCID: PMC11526443 DOI: 10.1016/j.imu.2024.101590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2024] Open
Abstract
Background Institutions interested in collaborative machine learning to enhance healthcare may be deterred by privacy concerns. Decentralized federated learning is a privacy-preserving and security-robust tool to promote cross-institutional learning, however, such frameworks require complex setups and advanced technical expertise. Here, we aim to improve their utilization by offering an intuitive, user-friendly, and secure system that integrates both front-end and back-end functionalities. Method We develop WebQuorumChain, an integrated system built upon the QuorumChain schema. We test the system on a 2-site network using two publicly available health datasets and measure the average vertical and horizontal-ensemble AUCs per dataset across 30 trials, as well as the average execution time of the system. Results Our system achieved consistently high AUCs for each dataset (0.94-0.96), with reasonable total execution times ranging from 5 to 20 min, inclusive of modeling and all other system overheads. The front-end displays event logs generated from back-end layers in real time, in sync with the progress of the underlying algorithm. Conclusions We develop a web-based system that supplies users with visual tools to configure the federated learning network, manage training sessions, and inspect the learning process. WebQuorumChain helps schedule and monitor low-level processes without violating the fundamental security promises of cross-institutional decentralized machine learning. The system also maintains predictive accuracy and runtime efficiency in the presence of additional layers. WebQuorumChain will help promote meaningful collaboration among healthcare institutions, who can retain full control of their data privacy while contributing to data-driven discoveries.
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Affiliation(s)
- Xiyan Shao
- UCSD Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Anh Pham
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA
| | - Tsung-Ting Kuo
- UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA
- Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT, USA
- Department of Surgery, School of Medicine, Yale University, New Haven, CT, USA
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20
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Chen J, Qiu RL, Wang T, Momin S, Yang X. A Review of Artificial Intelligence in Brachytherapy. ARXIV 2024:arXiv:2409.16543v1. [PMID: 39398213 PMCID: PMC11469420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/15/2024]
Abstract
Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in facilitating various aspects of brachytherapy. We analyze AI's role in making brachytherapy treatments more personalized, efficient, and effective. The applications are systematically categorized into seven categories: imaging, preplanning, treatment planning, applicator reconstruction, quality assurance, outcome prediction, and real-time monitoring. Each major category is further subdivided based on cancer type or specific tasks, with detailed summaries of models, data sizes, and results presented in corresponding tables. This review offers insights into the current advancements, challenges, and the impact of AI on treatment paradigms, encouraging further research to expand its clinical utility.
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Affiliation(s)
- Jingchu Chen
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308
- School of Mechanical Engineering, Georgia Institute of Technology, GA, Atlanta, USA
| | - Richard L.J. Qiu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Shadab Momin
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308
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21
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Silva C, Nascimento D, Dantas GG, Fonseca K, Hespanhol L, Rego A, Araújo-Filho I. Impact of artificial intelligence on the training of general surgeons of the future: a scoping review of the advances and challenges. Acta Cir Bras 2024; 39:e396224. [PMID: 39319900 PMCID: PMC11414521 DOI: 10.1590/acb396224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 08/01/2024] [Indexed: 09/26/2024] Open
Abstract
PURPOSE To explore artificial intelligence's impact on surgical education, highlighting its advantages and challenges. METHODS A comprehensive search across databases such as PubMed, Scopus, Scientific Electronic Library Online (SciELO), Embase, Web of Science, and Google Scholar was conducted to compile relevant studies. RESULTS Artificial intelligence offers several advantages in surgical training. It enables highly realistic simulation environments for the safe practice of complex procedures. Artificial intelligence provides personalized real-time feedback, improving trainees' skills. It efficiently processes clinical data, enhancing diagnostics and surgical planning. Artificial intelligence-assisted surgeries promise precision and minimally invasive procedures. Challenges include data security, resistance to artificial intelligence adoption, and ethical considerations. CONCLUSIONS Stricter policies and regulatory compliance are needed for data privacy. Addressing surgeons' and educators' reluctance to embrace artificial intelligence is crucial. Integrating artificial intelligence into curricula and providing ongoing training are vital. Ethical, bioethical, and legal aspects surrounding artificial intelligence demand attention. Establishing clear ethical guidelines, ensuring transparency, and implementing supervision and accountability are essential. As artificial intelligence evolves in surgical training, research and development remain crucial. Future studies should explore artificial intelligence-driven personalized training and monitor ethical and legal regulations. In summary, artificial intelligence is shaping the future of general surgeons, offering advanced simulations, personalized feedback, and improved patient care. However, addressing data security, adoption resistance, and ethical concerns is vital. Adapting curricula and providing continuous training are essential to maximize artificial intelligence's potential, promoting ethical and safe surgery.
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Affiliation(s)
- Caroliny Silva
- Universidade Federal do Rio Grande do Norte – General Surgery Department – Natal (RN) – Brazil
| | - Daniel Nascimento
- Universidade Federal do Rio Grande do Norte – General Surgery Department – Natal (RN) – Brazil
| | - Gabriela Gomes Dantas
- Universidade Federal do Rio Grande do Norte – General Surgery Department – Natal (RN) – Brazil
| | - Karoline Fonseca
- Universidade Federal do Rio Grande do Norte – General Surgery Department – Natal (RN) – Brazil
| | - Larissa Hespanhol
- Universidade Federal de Campina Grande – General Surgery Department – Campina Grande (PB) – Brazil
| | - Amália Rego
- Liga Contra o Câncer – Institute of Teaching, Research, and Innovation – Natal (RN) – Brazil
| | - Irami Araújo-Filho
- Universidade Federal do Rio Grande do Norte – General Surgery Department – Natal (RN) – Brazil
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22
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Wang X, Huang X. Risk factors and predictive indicators of rupture in cerebral aneurysms. Front Physiol 2024; 15:1454016. [PMID: 39301423 PMCID: PMC11411460 DOI: 10.3389/fphys.2024.1454016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Accepted: 08/23/2024] [Indexed: 09/22/2024] Open
Abstract
Cerebral aneurysms are abnormal dilations of blood vessels in the brain that have the potential to rupture, leading to subarachnoid hemorrhage and other serious complications. Early detection and prediction of aneurysm rupture are crucial for effective management and prevention of rupture-related morbidities and mortalities. This review aims to summarize the current knowledge on risk factors and predictive indicators of rupture in cerebral aneurysms. Morphological characteristics such as aneurysm size, shape, and location, as well as hemodynamic factors including blood flow patterns and wall shear stress, have been identified as important factors influencing aneurysm stability and rupture risk. In addition to these traditional factors, emerging evidence suggests that biological and genetic factors, such as inflammation, extracellular matrix remodeling, and genetic polymorphisms, may also play significant roles in aneurysm rupture. Furthermore, advancements in computational fluid dynamics and machine learning algorithms have enabled the development of novel predictive models for rupture risk assessment. However, challenges remain in accurately predicting aneurysm rupture, and further research is needed to validate these predictors and integrate them into clinical practice. By elucidating and identifying the various risk factors and predictive indicators associated with aneurysm rupture, we can enhance personalized risk assessment and optimize treatment strategies for patients with cerebral aneurysms.
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Affiliation(s)
- Xiguang Wang
- Department of Research & Development Management, Shanghai Aohua Photoelectricity Endoscope Co., Ltd., Shanghai, China
| | - Xu Huang
- Department of Research & Development Management, Shanghai Aohua Photoelectricity Endoscope Co., Ltd., Shanghai, China
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23
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Vasan V, Cheng CP, Lerner DK, Pascual K, Mercado A, Iloreta AM, Teng MS. Machine Learning for Predictive Analysis of Otolaryngology Residency Letters of Recommendation. Laryngoscope 2024; 134:4016-4022. [PMID: 38602257 DOI: 10.1002/lary.31439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/06/2024] [Accepted: 03/25/2024] [Indexed: 04/12/2024]
Abstract
INTRODUCTION Letters of recommendation (LORs) are a highly influential yet subjective and often enigmatic aspect of the residency application process. This study hypothesizes that LORs do contain valuable insights into applicants and can be used to predict outcomes. This pilot study utilizes natural language processing and machine learning (ML) models using LOR text to predict interview invitations for otolaryngology residency applicants. METHODS A total of 1642 LORs from the 2022-2023 application cycle were retrospectively retrieved from a single institution. LORs were preprocessed and vectorized using three different techniques to represent the text in a way that an ML model can understand written prose: CountVectorizer (CV), Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec (WV). Then, the LORs were trained and tested on five ML models: Logistic Regression (LR), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). RESULTS Of the 337 applicants, 67 were interviewed and 270 were not interviewed. In total, 1642 LORs (26.7% interviewed) were analyzed. The two best-performing ML models in predicting interview invitations were the TF-IDF vectorized DT and CV vectorized DT models. CONCLUSION This preliminary study revealed that ML models and vectorization combinations can provide better-than-chance predictions for interview invitations for otolaryngology residency applicants. The high-performing ML models were able to classify meaningful information from the LORs to predict applicant interview invitation. The potential of an automated process to help predict an applicant's likelihood of obtaining an interview invitation could be a valuable tool for training programs in the future. LEVEL OF EVIDENCE N/A Laryngoscope, 134:4016-4022, 2024.
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Affiliation(s)
- Vikram Vasan
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
| | - Christopher P Cheng
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
| | - David K Lerner
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
- Department of Otolaryngology-Head & Neck Surgery, University of Miami Miller School of Medicine, Miami, Florida, U.S.A
| | - Karen Pascual
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
| | - Amanda Mercado
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
| | - Alfred Marc Iloreta
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
| | - Marita S Teng
- Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
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24
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Qiu P, Cao R, Li Z, Huang J, Zhang H, Zhang X. Applications of artificial intelligence for surgical extraction in stomatology: a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:346-361. [PMID: 38834501 DOI: 10.1016/j.oooo.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/16/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024]
Abstract
OBJECTIVES Artificial intelligence (AI) has been extensively used in the field of stomatology over the past several years. This study aimed to evaluate the effectiveness of AI-based models in the procedure, assessment, and treatment planning of surgical extraction. STUDY DESIGN Following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, a comprehensive search was conducted on the Web of Science, PubMed/MEDLINE, Embase, and Scopus databases, covering English publications up to September 2023. Two reviewers performed the study selection and data extraction independently. Only original research studies utilizing AI in surgical extraction of stomatology were included. The Cochrane risk of bias tool for randomized trials (RoB 2) was selected to perform the quality assessment of the selected literature. RESULTS From 2,336 retrieved references, 35 studies were deemed eligible. Among them, 28 researchers reported the pioneering role of AI in segmentation, classification, and detection, aligning with clinical needs. In addition, another 7 studies suggested promising results in tooth extraction decision-making, but further model refinement and validation were required. CONCLUSIONS Integration of AI in stomatology surgical extraction has significantly progressed, enhancing decision-making accuracy. Combining and comparing algorithmic outcomes across studies is essential for determining optimal clinical applications in the future.
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Affiliation(s)
- Piaopiao Qiu
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China
| | - Rongkai Cao
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China
| | - Zhaoyang Li
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China
| | - Jiaqi Huang
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China
| | - Huasheng Zhang
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China
| | - Xueming Zhang
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China.
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25
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Ong W, Lee A, Tan WC, Fong KTD, Lai DD, Tan YL, Low XZ, Ge S, Makmur A, Ong SJ, Ting YH, Tan JH, Kumar N, Hallinan JTPD. Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review. Cancers (Basel) 2024; 16:2988. [PMID: 39272846 PMCID: PMC11394591 DOI: 10.3390/cancers16172988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 08/14/2024] [Accepted: 08/26/2024] [Indexed: 09/15/2024] Open
Abstract
In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Aric Lee
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Wei Chuan Tan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Kuan Ting Dominic Fong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Daoyong David Lai
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Yi Liang Tan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Shao Jin Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Yong Han Ting
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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26
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van Hal ST, van der Jagt M, van Genderen ME, Gommers D, Veenland JF. Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury: A Systematic Review. Neurocrit Care 2024; 41:285-296. [PMID: 38212559 PMCID: PMC11335950 DOI: 10.1007/s12028-023-01910-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 12/01/2023] [Indexed: 01/13/2024]
Abstract
Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating "real-time model testing" stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.
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Affiliation(s)
- S T van Hal
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands.
| | - M van der Jagt
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - M E van Genderen
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - D Gommers
- Department of Adult Intensive Care, Erasmus MC University Medical Center, Room Ne-413, Doctor Molewaterplein 40, Rotterdam, 3015 GD, The Netherlands
| | - J F Veenland
- Department of Medical Informatics, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Radiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
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27
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Baron R, Haick H. Mobile Diagnostic Clinics. ACS Sens 2024; 9:2777-2792. [PMID: 38775426 PMCID: PMC11217950 DOI: 10.1021/acssensors.4c00636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/06/2024] [Accepted: 05/10/2024] [Indexed: 06/29/2024]
Abstract
This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.
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Affiliation(s)
- Roni Baron
- Department
of Biomedical Engineering, Technion—Israel
Institute of Technology, Haifa 3200003, Israel
| | - Hossam Haick
- Department
of Chemical Engineering and the Russell Berrie Nanotechnology Institute, Technion—Israel Institute of Technology, Haifa 3200003, Israel
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28
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Manzana LC, Chipeya LR, Bresser P. Experiences of Nuclear Medicine Technologists Working in PET/CT Facilities in Gauteng Province, South Africa. J Nucl Med Technol 2024; 52:163-167. [PMID: 38839113 DOI: 10.2967/jnmt.123.266240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/21/2023] [Indexed: 06/07/2024] Open
Abstract
The introduction of PET/CT requires staff training, redesign of patient workflow, new skills, problem-solving abilities, and adjustments to radiation protection protocols. When PET/CT was introduced in the U.K., nuclear medicine technologists (NMTs) encountered challenges in defining their roles and unfamiliarity with the new technology and the new working procedures. Since the introduction of PET/CT in South Africa, the experiences of NMTs with this hybrid imaging device have not yet been described. Therefore, the aim of this research study was to explore and describe the experiences of NMTs working in PET/CT facilities in Gauteng Province, South Africa. Methods: This study had a qualitative, exploratory, descriptive design and used a phenomenologic research approach. Semistructured interviews were conducted to collect data until data saturation was reached. A software program was used to manage the codes, categories, and themes. Nine NMTs participated in the study: 5 from public hospitals and 4 from private hospitals. Their age range of 27-58 y provided the ideal heterogeneity for sharing experiences in working in PET/CT facilities. Results: Two overarching themes emerged from the categories: the perspectives of NMTs working in PET/CT facilities and the PET/CT challenges encountered by NMTs. The results suggest that NMTs experience joy and fulfilment from working in PET/CT facilities and regard PET/CT as the future of nuclear medicine. However, NMTs also experience a gap in PET/CT training and are concerned about the high radiation exposure associated with PET/CT imaging and about the lack of psychologic support. Conclusion: Although the NMTs enjoy working in PET/CT, they desire additional clinical training and psychologic support. Since radiation exposure in PET/CT is higher than in general nuclear medicine, radiation monitoring is imperative to minimize exposure to NMTs and patients.
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Affiliation(s)
- Lindiwe Charlotte Manzana
- Department of Medical Imaging and Radiation Sciences, University of Johannesburg, Johannesburg, South Africa; and
| | - Lucky Rachel Chipeya
- Department of Medical Imaging and Radiation Sciences, University of Johannesburg, Johannesburg, South Africa; and
| | - Philippa Bresser
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
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29
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Xu K, Kang H. A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis. Nucl Med Mol Imaging 2024; 58:203-212. [PMID: 38932757 PMCID: PMC11196571 DOI: 10.1007/s13139-024-00845-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/19/2024] [Accepted: 01/25/2024] [Indexed: 06/28/2024] Open
Abstract
Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.
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Affiliation(s)
- Ke Xu
- Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1100, Nashville, TN 37203 USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 1100, Nashville, TN 37203 USA
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30
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Bo Z, Song J, He Q, Chen B, Chen Z, Xie X, Shu D, Chen K, Wang Y, Chen G. Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma. Comput Biol Med 2024; 173:108337. [PMID: 38547656 DOI: 10.1016/j.compbiomed.2024.108337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/04/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.
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Affiliation(s)
- Zhiyuan Bo
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jiatao Song
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qikuan He
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Bo Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ziyan Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaozai Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Danyang Shu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kaiyu Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| | - Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health and Management, Wenzhou Medical University, Wenzhou, China.
| | - Gang Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Zhejiang-Germany Interdisciplinary Joint Laboratory of Hepatobiliary-Pancreatic Tumor and Bioengineering, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
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Jomeiri A, Navin AH, Shamsi M. Longitudinal MRI analysis using a hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction. Behav Brain Res 2024; 463:114900. [PMID: 38341100 DOI: 10.1016/j.bbr.2024.114900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 12/16/2023] [Accepted: 02/06/2024] [Indexed: 02/12/2024]
Abstract
Alzheimer's disease is a progressive neurological disorder characterized by brain atrophy and cell death, leading to cognitive decline and impaired functioning. Previous research has primarily focused on using cross-sectional data for Alzheimer's disease identification, but analyzing longitudinal sequential MR images is crucial for improved diagnostic accuracy and understanding disease progression. However, existing deep learning models face challenges in learning spatial and temporal features from such data. To address these challenges, this study presents a novel hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction using longitudinal MRI analysis. The proposed framework combines Convolutional DenseNet for spatial information extraction and joined BiLSTM layers for capturing temporal characteristics and relationships between longitudinal images at different time points. This approach overcomes issues like overfitting, vanishing gradients, and incomplete patient data. We evaluated the model on 684 longitudinal MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including normal controls, individuals with mild cognitive impairment, and Alzheimer's disease patients. The results demonstrate high classification accuracy, with 95.28% for AD/CN, 88.19% for NC/MCI, 83.51% for sMCI/pMCI, and 92.14% for MCI/AD. These findings highlight the substantial improvement in Alzheimer's disease diagnosis achieved through the utilization of longitudinal MRI images. The contributions of this study lie in both the deep learning and medical domains. In the deep learning domain, our hybrid framework effectively learns spatial and temporal features from longitudinal data, addressing the challenges associated with multi-dimensional and sequential time series data. In the medical domain, our study emphasizes the importance of analyzing baseline and longitudinal MR images for accurate diagnosis and understanding disease progression.
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Affiliation(s)
- Alireza Jomeiri
- Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran
| | - Ahmad Habibizad Navin
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
| | - Mahboubeh Shamsi
- Department of Engineering, Qom University of Technology, Qom, Iran
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Zhang S, Chen D, Sun H, Kemp GJ, Chen Y, Tan Q, Yang Y, Gong Q, Yue Q. Whole brain morphologic features improve the predictive accuracy of IDH status and VEGF expression levels in gliomas. Cereb Cortex 2024; 34:bhae151. [PMID: 38642107 DOI: 10.1093/cercor/bhae151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/14/2024] [Accepted: 03/23/2024] [Indexed: 04/22/2024] Open
Abstract
Glioma is a systemic disease that can induce micro and macro alternations of whole brain. Isocitrate dehydrogenase and vascular endothelial growth factor are proven prognostic markers and antiangiogenic therapy targets in glioma. The aim of this study was to determine the ability of whole brain morphologic features and radiomics to predict isocitrate dehydrogenase status and vascular endothelial growth factor expression levels. This study recruited 80 glioma patients with isocitrate dehydrogenase wildtype and high vascular endothelial growth factor expression levels, and 102 patients with isocitrate dehydrogenase mutation and low vascular endothelial growth factor expression levels. Virtual brain grafting, combined with Freesurfer, was used to compute morphologic features including cortical thickness, LGI, and subcortical volume in glioma patient. Radiomics features were extracted from multiregional tumor. Pycaret was used to construct the machine learning pipeline. Among the radiomics models, the whole tumor model achieved the best performance (accuracy 0.80, Area Under the Curve 0.86), while, after incorporating whole brain morphologic features, the model had a superior predictive performance (accuracy 0.82, Area Under the Curve 0.88). The features contributed most in predicting model including the right caudate volume, left middle temporal cortical thickness, first-order statistics, shape, and gray-level cooccurrence matrix. Pycaret, based on morphologic features, combined with radiomics, yielded highest accuracy in predicting isocitrate dehydrogenase mutation and vascular endothelial growth factor levels, indicating that morphologic abnormalities induced by glioma were associated with tumor biology.
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Affiliation(s)
- Simin Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China
| | - Di Chen
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 7ZX, United Kingdom
| | - Yinying Chen
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Qiaoyue Tan
- Division of Radiation Physics, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Yuan Yang
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
- Huaxi Glioma Center, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Qiyong Gong
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Sichuan 610041, China
| | - Qiang Yue
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
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Abubakar H, Al-Turjman F, Ameen ZS, Mubarak AS, Altrjman C. A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images. Heliyon 2024; 10:e26939. [PMID: 38463848 PMCID: PMC10920381 DOI: 10.1016/j.heliyon.2024.e26939] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/12/2024] Open
Abstract
COVID-19 has killed more than 5 million individuals worldwide within a short time. It is caused by SARS-CoV-2 which continuously mutates and produces more transmissible new different strains. It is therefore of great significance to diagnose COVID-19 early to curb its spread and reduce the death rate. Owing to the COVID-19 pandemic, traditional diagnostic methods such as reverse-transcription polymerase chain reaction (RT-PCR) are ineffective for diagnosis. Medical imaging is among the most effective techniques of respiratory disorders detection through machine learning and deep learning. However, conventional machine learning methods depend on extracted and engineered features, whereby the optimum features influence the classifier's performance. In this study, Histogram of Oriented Gradient (HOG) and eight deep learning models were utilized for feature extraction while K-Nearest Neighbour (KNN) and Support Vector Machines (SVM) were used for classification. A combined feature of HOG and deep learning feature was proposed to improve the performance of the classifiers. VGG-16 + HOG achieved 99.4 overall accuracy with SVM. This indicates that our proposed concatenated feature can enhance the SVM classifier's performance in COVID-19 detection.
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Affiliation(s)
- Hassana Abubakar
- Biomedical Engineering Department, Faculty of Engineering, Near East University, Mersin 10, Turkey
| | - Fadi Al-Turjman
- Artificial Intelligence Engineering Department, AI and Robotics Institute, Near East University, Mersin 10, Turkey
- Research Center for AI and IoT, Faculty of Engineering, University of Kyrenia, Mersin 10, Turkey
| | - Zubaida S. Ameen
- Operational Research Center in Healthcare, Near East University, Mersin 10, Turkey
| | - Auwalu S. Mubarak
- Operational Research Center in Healthcare, Near East University, Mersin 10, Turkey
| | - Chadi Altrjman
- Waterloo University, 200 University Avenue West. Waterloo, ON, Canada
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C Pereira S, Mendonça AM, Campilho A, Sousa P, Teixeira Lopes C. Automated image label extraction from radiology reports - A review. Artif Intell Med 2024; 149:102814. [PMID: 38462277 DOI: 10.1016/j.artmed.2024.102814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/29/2023] [Accepted: 02/12/2024] [Indexed: 03/12/2024]
Abstract
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to obtain because the annotations have to be performed by qualified physicians. Natural Language Processing (NLP) tools can be applied to radiology reports to extract labels for medical images automatically. Compared to manual labeling, this approach requires smaller annotation efforts and can therefore facilitate the creation of labeled medical image data sets. In this article, we summarize the literature on this topic spanning from 2013 to 2023, starting with a meta-analysis of the included articles, followed by a qualitative and quantitative systematization of the results. Overall, we found four types of studies on the extraction of labels from radiology reports: those describing systems based on symbolic NLP, statistical NLP, neural NLP, and those describing systems combining or comparing two or more of the latter. Despite the large variety of existing approaches, there is still room for further improvement. This work can contribute to the development of new techniques or the improvement of existing ones.
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Affiliation(s)
- Sofia C Pereira
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), Portugal; Faculty of Engineering of the University of Porto, Portugal.
| | - Ana Maria Mendonça
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), Portugal; Faculty of Engineering of the University of Porto, Portugal.
| | - Aurélio Campilho
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), Portugal; Faculty of Engineering of the University of Porto, Portugal.
| | - Pedro Sousa
- Hospital Center of Vila Nova de Gaia/Espinho, Portugal.
| | - Carla Teixeira Lopes
- Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), Portugal; Faculty of Engineering of the University of Porto, Portugal.
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Miura K, Tanaka SM, Chotipanich C, Chobpenthai T, Jantarato A, Khantachawana A. Osteoporosis Prediction Using Machine-Learned Optical Bone Densitometry Data. Ann Biomed Eng 2024; 52:396-405. [PMID: 37882922 PMCID: PMC10808164 DOI: 10.1007/s10439-023-03387-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 10/11/2023] [Indexed: 10/27/2023]
Abstract
Optical bone densitometry (OBD) has been developed for the early detection of osteoporosis. In recent years, machine learning (ML) techniques have been actively implemented for the areas of medical diagnosis and screening with the goal of improving diagnostic accuracy. The purpose of this study was to verify the feasibility of using the combination of OBD and ML techniques as a screening tool for osteoporosis. Dual energy X-ray absorptiometry (DXA) and OBD measurements were performed on 203 Thai subjects. From the OBD measurements and readily available demographic data, machine learning techniques were used to predict the T-score measured by the DXA. The T-score predicted using the Ridge regressor had a correlation of r = 0.512 with respect to the reference value. The predicted T-score also showed an AUC of 0.853 for discriminating individuals with osteoporosis. The results obtained suggest that the developed model is reliable enough to be used for screening for osteoporosis.
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Affiliation(s)
- Kaname Miura
- Biological Engineering Program, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand
- Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, 920-1192, Japan
| | - Shigeo M Tanaka
- Institute of Science and Engineering, Kanazawa University, Kanazawa, 920-1192, Japan
| | - Chanisa Chotipanich
- National Cyclotron and PET Center, Chulabhorn Hospital, Bangkok, 10140, Thailand
| | - Thanapon Chobpenthai
- Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, 10140, Thailand
| | - Attapon Jantarato
- National Cyclotron and PET Center, Chulabhorn Hospital, Bangkok, 10140, Thailand
| | - Anak Khantachawana
- Department of Mechanical Engineering, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand.
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Quinn TP, Hess JL, Marshe VS, Barnett MM, Hauschild AC, Maciukiewicz M, Elsheikh SSM, Men X, Schwarz E, Trakadis YJ, Breen MS, Barnett EJ, Zhang-James Y, Ahsen ME, Cao H, Chen J, Hou J, Salekin A, Lin PI, Nicodemus KK, Meyer-Lindenberg A, Bichindaritz I, Faraone SV, Cairns MJ, Pandey G, Müller DJ, Glatt SJ. A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry 2024; 29:387-401. [PMID: 38177352 PMCID: PMC11228968 DOI: 10.1038/s41380-023-02334-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/21/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024]
Abstract
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
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Affiliation(s)
- Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia
| | - Jonathan L Hess
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Victoria S Marshe
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Michelle M Barnett
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Anne-Christin Hauschild
- Department of Medical Informatics, Medical University Center Göttingen, Göttingen, Lower Saxony, 37075, Germany
| | - Malgorzata Maciukiewicz
- Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland
- Department of Rheumatology and Immunology, University Hospital Bern, Bern, 3010, Switzerland
- Department for Biomedical Research (DBMR), University of Bern, Bern, 3010, Switzerland
| | - Samar S M Elsheikh
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Xiaoyu Men
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Yannis J Trakadis
- Department Human Genetics, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
| | - Michael S Breen
- Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric J Barnett
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Mehmet Eren Ahsen
- Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Department of Biomedical and Translational Sciences, Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Jiahui Hou
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Asif Salekin
- Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA
| | - Ping-I Lin
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, 2052, Australia
- Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia
| | | | - Andreas Meyer-Lindenberg
- Clinical Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Isabelle Bichindaritz
- Biomedical and Health Informatics/Computer Science Department, State University of New York at Oswego, Oswego, NY, 13126, USA
- Intelligent Bio Systems Lab, State University of New York at Oswego, Oswego, NY, 13126, USA
| | - Stephen V Faraone
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, 97080, Germany
| | - Stephen J Glatt
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Public Health and Preventive Medicine, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
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Rizvi SA, Tang R, Jiang X, Ma X, Hu X. Local Contrastive Learning for Medical Image Recognition. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:1236-1245. [PMID: 38222415 PMCID: PMC10785845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality interaction. Our results on an external validation set of chest x-rays suggest that LRCLR identifies significant local image regions and provides meaningful interpretation against radiology text while improving zero-shot performance on several chest x-ray medical findings.
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Affiliation(s)
| | | | | | - Xiaotian Ma
- University of Texas Health Science Center, Houston, TX
| | - Xia Hu
- Rice University, Houston, TX
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38
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Till T, Tschauner S, Singer G, Lichtenegger K, Till H. Development and optimization of AI algorithms for wrist fracture detection in children using a freely available dataset. Front Pediatr 2023; 11:1291804. [PMID: 38188914 PMCID: PMC10768054 DOI: 10.3389/fped.2023.1291804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 12/05/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction In the field of pediatric trauma computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems have emerged offering a promising avenue for improved patient care. Especially children with wrist fractures may benefit from machine learning (ML) solutions, since some of these lesions may be overlooked on conventional X-ray due to minimal compression without dislocation or mistaken for cartilaginous growth plates. In this article, we describe the development and optimization of AI algorithms for wrist fracture detection in children. Methods A team of IT-specialists, pediatric radiologists and pediatric surgeons used the freely available GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, a total number of 10,643 studies (20,327 images). First, a basic object detection model, a You Only Look Once object detector of the seventh generation (YOLOv7) was trained and tested on these data. Then, team decisions were taken to adjust data preparation, image sizes used for training and testing, and configuration of the detection model. Furthermore, we investigated each of these models using an Explainable Artificial Intelligence (XAI) method called Gradient Class Activation Mapping (Grad-CAM). This method visualizes where a model directs its attention to before classifying and regressing a certain class through saliency maps. Results Mean average precision (mAP) improved when applying optimizations pre-processing the dataset images (maximum increases of + 25.51% mAP@0.5 and + 39.78% mAP@[0.5:0.95]), as well as the object detection model itself (maximum increases of + 13.36% mAP@0.5 and + 27.01% mAP@[0.5:0.95]). Generally, when analyzing the resulting models using XAI methods, higher scoring model variations in terms of mAP paid more attention to broader regions of the image, prioritizing detection accuracy over precision compared to the less accurate models. Discussion This paper supports the implementation of ML solutions for pediatric trauma care. Optimization of a large X-ray dataset and the YOLOv7 model improve the model's ability to detect objects and provide valid diagnostic support to health care specialists. Such optimization protocols must be understood and advocated, before comparing ML performances against health care specialists.
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Affiliation(s)
- Tristan Till
- Department of Applied Computer Sciences, FH JOANNEUM - University of Applied Sciences, Graz, Austria
- Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - Sebastian Tschauner
- Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - Georg Singer
- Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria
| | - Klaus Lichtenegger
- Department of Applied Computer Sciences, FH JOANNEUM - University of Applied Sciences, Graz, Austria
| | - Holger Till
- Department of Pediatric and Adolescent Surgery, Medical University of Graz, Graz, Austria
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Zou P, Zhang L, Zhang R, Wang C, Lin X, Lai C, Lu Y, Yan Z. Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter-Based Machine Learning Model for Identifying Idiopathic Central Precocious Puberty in Girls. J Magn Reson Imaging 2023; 58:1977-1987. [PMID: 36995000 DOI: 10.1002/jmri.28709] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/15/2023] [Accepted: 03/15/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Idiopathic central precocious puberty (ICPP) impairs child development, without early intervention. The current reference standard, the gonadotropin-releasing hormone stimulation test, is invasive which may hinder diagnosis and intervention. PURPOSE To develop a model for accurate diagnosis of ICPP, by integrating pituitary MRI, carpal bone age, gonadal ultrasound, and basic clinical data. STUDY TYPE Retrospective. POPULATION A total of 492 girls with PP (185 with ICPP and 307 peripheral precocious puberty [PPP]) were randomly divided by reference standard into training (75%) and internal validation (25%) data. Fifty-one subjects (16 with ICPP, 35 with PPP) provided by another hospital as external validation. FIELD STRENGTH/SEQUENCE T1-weighted (spin echo [SE], fast SE, cube) and T2-weighted (fast SE-fat suppression) imaging at 3.0 T or 1.5 T. ASSESSMENT Radiomics features were extracted from pituitary MRI after manual segmentation. Carpal bone age, ovarian, follicle and uterine volumes and endometrium presence were assessed from radiographs and gonadal ultrasound. Four machine learning methods were developed: a pituitary MRI radiomics model, an integrated image model (with pituitary MRI, gonadal ultrasound and bone age), a basic clinical model (with age and sex hormone data), and an integrated multimodal model combining all features. STATISTICAL TESTS Intraclass correlation coefficients were used to assess consistency of segmentation. Receiver operating characteristic (ROC) curves and the Delong tests were used to assess and compare the diagnostic performance of models. P < 0.05 was considered statistically significant. RESULTS The area under of the ROC curve (AUC) of the pituitary MRI radiomics model, integrated image model, basic clinical model, and integrated multimodal model in the training data was 0.668, 0.809, 0.792, and 0.860. The integrated multimodal model had higher diagnostic efficacy (AUC of 0.862 and 0.866 for internal and external validation). CONCLUSION The integrated multimodal model may have potential as an alternative clinical approach to diagnose ICPP. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Pinfa Zou
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lingfeng Zhang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Ruifang Zhang
- Department of Radiology, Children's hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Chenyan Wang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - XingTong Lin
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Can Lai
- Department of Radiology, Children's hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Yi Lu
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
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Alharthi AG, Alzahrani SM. Do it the transformer way: A comprehensive review of brain and vision transformers for autism spectrum disorder diagnosis and classification. Comput Biol Med 2023; 167:107667. [PMID: 37939407 DOI: 10.1016/j.compbiomed.2023.107667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 10/25/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023]
Abstract
Autism spectrum disorder (ASD) is a condition observed in children who display abnormal patterns of interaction, behavior, and communication with others. Despite extensive research efforts, the underlying causes of this neurodevelopmental disorder and its biomarkers remain unknown. However, advancements in artificial intelligence and machine learning have improved clinicians' ability to diagnose ASD. This review paper investigates various MRI modalities to identify distinct features that characterize individuals with ASD compared to typical control subjects. The review then moves on to explore deep learning models for ASD diagnosis, including convolutional neural networks (CNNs), autoencoders, graph convolutions, attention networks, and other models. CNNs and their variations are particularly effective due to their capacity to learn structured image representations and identify reliable biomarkers for brain disorders. Computer vision transformers often employ CNN architectures with transfer learning techniques like fine-tuning and layer freezing to enhance image classification performance, surpassing traditional machine learning models. This review paper contributes in three main ways. Firstly, it provides a comprehensive overview of a recommended architecture for using vision transformers in the systematic ASD diagnostic process. To this end, the paper investigates various pre-trained vision architectures such as VGG, ResNet, Inception, InceptionResNet, DenseNet, and Swin models that were fine-tuned for ASD diagnosis and classification. Secondly, it discusses the vision transformers of 2020th like BiT, ViT, MobileViT, and ConvNeXt, and applying transfer learning methods in relation to their prospective practicality in ASD classification. Thirdly, it explores brain transformers that are pre-trained on medically rich data and MRI neuroimaging datasets. The paper recommends a systematic architecture for ASD diagnosis using brain transformers. It also reviews recently developed brain transformer-based models, such as METAFormer, Com-BrainTF, Brain Network, ST-Transformer, STCAL, BolT, and BrainFormer, discussing their deep transfer learning architectures and results in ASD detection. Additionally, the paper summarizes and discusses brain-related transformers for various brain disorders, such as MSGTN, STAGIN, and MedTransformer, in relation to their potential usefulness in ASD. The study suggests that developing specialized transformer-based models, following the success of natural language processing (NLP), can offer new directions for image classification problems in ASD brain biomarkers learning and classification. By incorporating the attention mechanism, treating MRI modalities as sequence prediction tasks trained on brain disorder classification problems, and fine-tuned on ASD datasets, brain transformers can show a great promise in ASD diagnosis.
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Affiliation(s)
- Asrar G Alharthi
- Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia.
| | - Salha M Alzahrani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia
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de Groot TM, Ramsey D, Groot OQ, Fourman M, Karhade AV, Twining PK, Berner EA, Fenn BP, Collins AK, Raskin K, Lozano S, Newman E, Ferrone M, Doornberg JN, Schwab JH. Does the SORG Machine-learning Algorithm for Extremity Metastases Generalize to a Contemporary Cohort of Patients? Temporal Validation From 2016 to 2020. Clin Orthop Relat Res 2023; 481:2419-2430. [PMID: 37229565 PMCID: PMC10642892 DOI: 10.1097/corr.0000000000002698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/15/2023] [Accepted: 04/21/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND The ability to predict survival accurately in patients with osseous metastatic disease of the extremities is vital for patient counseling and guiding surgical intervention. We, the Skeletal Oncology Research Group (SORG), previously developed a machine-learning algorithm (MLA) based on data from 1999 to 2016 to predict 90-day and 1-year survival of surgically treated patients with extremity bone metastasis. As treatment regimens for oncology patients continue to evolve, this SORG MLA-driven probability calculator requires temporal reassessment of its accuracy. QUESTION/PURPOSE Does the SORG-MLA accurately predict 90-day and 1-year survival in patients who receive surgical treatment for a metastatic long-bone lesion in a more recent cohort of patients treated between 2016 and 2020? METHODS Between 2017 and 2021, we identified 674 patients 18 years and older through the ICD codes for secondary malignant neoplasm of bone and bone marrow and CPT codes for completed pathologic fractures or prophylactic treatment of an impending fracture. We excluded 40% (268 of 674) of patients, including 18% (118) who did not receive surgery; 11% (72) who had metastases in places other than the long bones of the extremities; 3% (23) who received treatment other than intramedullary nailing, endoprosthetic reconstruction, or dynamic hip screw; 3% (23) who underwent revision surgery, 3% (17) in whom there was no tumor, and 2% (15) who were lost to follow-up within 1 year. Temporal validation was performed using data on 406 patients treated surgically for bony metastatic disease of the extremities from 2016 to 2020 at the same two institutions where the MLA was developed. Variables used to predict survival in the SORG algorithm included perioperative laboratory values, tumor characteristics, and general demographics. To assess the models' discrimination, we computed the c-statistic, commonly referred to as the area under the receiver operating characteristic (AUC) curve for binary classification. This value ranged from 0.5 (representing chance-level performance) to 1.0 (indicating excellent discrimination) Generally, an AUC of 0.75 is considered high enough for use in clinical practice. To evaluate the agreement between predicted and observed outcomes, a calibration plot was used, and the calibration slope and intercept were calculated. Perfect calibration would result in a slope of 1 and intercept of 0. For overall performance, the Brier score and null-model Brier score were determined. The Brier score can range from 0 (representing perfect prediction) to 1 (indicating the poorest prediction). Proper interpretation of the Brier score necessitates a comparison with the null-model Brier score, which represents the score for an algorithm that predicts a probability equal to the population prevalence of the outcome for each patient. Finally, a decision curve analysis was conducted to compare the potential net benefit of the algorithm with other decision-support methods, such as treating all or none of the patients. Overall, 90-day and 1-year mortality were lower in the temporal validation cohort than in the development cohort (90 day: 23% versus 28%; p < 0.001, and 1 year: 51% versus 59%; p<0.001). RESULTS Overall survival of the patients in the validation cohort improved from 28% mortality at the 90-day timepoint in the cohort on which the model was trained to 23%, and 59% mortality at the 1-year timepoint to 51%. The AUC was 0.78 (95% CI 0.72 to 0.82) for 90-day survival and 0.75 (95% CI 0.70 to 0.79) for 1-year survival, indicating the model could distinguish the two outcomes reasonably. For the 90-day model, the calibration slope was 0.71 (95% CI 0.53 to 0.89), and the intercept was -0.66 (95% CI -0.94 to -0.39), suggesting the predicted risks were overly extreme, and that in general, the risk of the observed outcome was overestimated. For the 1-year model, the calibration slope was 0.73 (95% CI 0.56 to 0.91) and the intercept was -0.67 (95% CI -0.90 to -0.43). With respect to overall performance, the model's Brier scores for the 90-day and 1-year models were 0.16 and 0.22. These scores were higher than the Brier scores of internal validation of the development study (0.13 and 0.14) models, indicating the models' performance has declined over time. CONCLUSION The SORG MLA to predict survival after surgical treatment of extremity metastatic disease showed decreased performance on temporal validation. Moreover, in patients undergoing innovative immunotherapy, the possibility of mortality risk was overestimated in varying severity. Clinicians should be aware of this overestimation and discount the prediction of the SORG MLA according to their own experience with this patient population. Generally, these results show that temporal reassessment of these MLA-driven probability calculators is of paramount importance because the predictive performance may decline over time as treatment regimens evolve. The SORG-MLA is available as a freely accessible internet application at https://sorg-apps.shinyapps.io/extremitymetssurvival/ .Level of Evidence Level III, prognostic study.
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Affiliation(s)
- Tom M. de Groot
- Massachusetts General Hospital, Boston, MA, USA
- University Medical Center Groningen, Groningen, the Netherlands
| | - Duncan Ramsey
- University of Texas RGV School of Medicine, Edinburg, TX, USA
| | | | | | | | | | | | | | | | | | | | - Eric Newman
- Massachusetts General Hospital, Boston, MA, USA
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Hiraoka A, Kumada T, Tada T, Toyoda H, Kariyama K, Hatanaka T, Kakizaki S, Naganuma A, Itobayashi E, Tsuji K, Ishikawa T, Ohama H, Tada F, Nouso K, on behalf of the Real-life Practice Experts for HCC (RELPEC) Study Group. Attempt to Establish Prognostic Predictive System for Hepatocellular Carcinoma Using Artificial Intelligence for Assistance with Selection of Treatment Modality. Liver Cancer 2023; 12:565-575. [PMID: 38058420 PMCID: PMC10697750 DOI: 10.1159/000530078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/06/2023] [Indexed: 12/08/2023] Open
Abstract
INTRODUCTION Because of recent developments in treatments for hepatocellular carcinoma (HCC), methods for determining suitable therapy for initial or recurrent HCC have become important. This study used artificial intelligence (AI) findings to establish a system for predicting prognosis of HCC patients at time of reoccurrence based on clinical data as a reference for selection of treatment modalities. METHODS As a training cohort, 5,701 observations obtained at the initial and each subsequent treatment for recurrence from 1,985 HCC patients at a single center from 2000 to 2021 were used. The validation cohort included 5,692 observations from patients at multiple centers obtained at the time of the initial treatment. An AI calculating system (PRAID) was constructed based on 25 clinical factors noted at each treatment from the training cohort, and then predictive prognostic values for 1- and 3-year survival in both cohorts were evaluated. RESULTS After exclusion of patients lacking clinical data regarding albumin-bilirubin (ALBI) grade or tumor-node-metastasis stage of the Liver Cancer Study Group of Japan, 6th edition (TNM-LCSGJ 6th), ALBI-TNM-LCSGJ 6th (ALBI-T) and modified ALBI-T scores confirmed that prognosis for patients in both cohorts was similar. The area under the curve for prediction of both 1- and 3-year survival in the validation cohort was 0.841 (sensitivity 0.933 [95% CI: 0.925-0.940], specificity 0.517 [95% CI: 0.484-0.549]) and 0.796 (sensitivity 0.806 [95% CI: 0.790-0.821], specificity 0.646 [95% CI: 0.624-0.668]), respectively. CONCLUSION The present PRAID system might provide useful prognostic information related to short and medium survival for decision-making regarding the best therapeutic modality for both initial and recurrent HCC cases.
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Affiliation(s)
- Atsushi Hiraoka
- Gastroenterology Center, Ehime Prefectural Central Hospital, Matsuyama, Japan
| | - Takashi Kumada
- Department of Nursing, Gifu Kyoritsu University, Ogaki, Japan
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan
| | - Toshifumi Tada
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan
| | - Hidenori Toyoda
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan
| | - Kazuya Kariyama
- Department of Hepatology, Okayama City Hospital, Okayama, Japan
| | - Takeshi Hatanaka
- Department of Gastroenterology, Gunma Saiseikai Maebashi Hospital, Maebashi, Japan
| | - Satoru Kakizaki
- Department of Clinical Research, National Hospital Organization Takasaki General Medical Center, Takasaki, Japan
| | - Atsushi Naganuma
- Department of Gastroenterology, National Hospital Organization Takasaki General Medical Center, Takasaki, Japan
| | - Ei Itobayashi
- Department of Gastroenterology, Asahi General Hospital, Asahi, Japan
| | - Kunihiko Tsuji
- Center of Gastroenterology, Teine Keijinkai Hospital, Sapporo, Japan
| | - Toru Ishikawa
- Department of Gastroenterology, Saiseikai Niigata Hospital, Niigata, Japan
| | - Hideko Ohama
- Gastroenterology Center, Ehime Prefectural Central Hospital, Matsuyama, Japan
| | - Fujimasa Tada
- Gastroenterology Center, Ehime Prefectural Central Hospital, Matsuyama, Japan
| | - Kazuhiro Nouso
- Department of Hepatology, Okayama City Hospital, Okayama, Japan
| | - on behalf of the Real-life Practice Experts for HCC (RELPEC) Study Group
- Gastroenterology Center, Ehime Prefectural Central Hospital, Matsuyama, Japan
- Department of Nursing, Gifu Kyoritsu University, Ogaki, Japan
- Department of Gastroenterology and Hepatology, Ogaki Municipal Hospital, Ogaki, Japan
- Department of Hepatology, Okayama City Hospital, Okayama, Japan
- Department of Gastroenterology, Gunma Saiseikai Maebashi Hospital, Maebashi, Japan
- Department of Clinical Research, National Hospital Organization Takasaki General Medical Center, Takasaki, Japan
- Department of Gastroenterology, National Hospital Organization Takasaki General Medical Center, Takasaki, Japan
- Department of Gastroenterology, Asahi General Hospital, Asahi, Japan
- Center of Gastroenterology, Teine Keijinkai Hospital, Sapporo, Japan
- Department of Gastroenterology, Saiseikai Niigata Hospital, Niigata, Japan
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Madamesila J, Tchistiakova E, Faruqi S, Das S, Ploquin N. Can machine learning models improve early detection of brain metastases using diffusion weighted imaging-based radiomics? Quant Imaging Med Surg 2023; 13:7706-7718. [PMID: 38106308 PMCID: PMC10722027 DOI: 10.21037/qims-23-441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 08/15/2023] [Indexed: 12/19/2023]
Abstract
Background Metastatic complications are a major cause of cancer-related morbidity, with up to 40% of cancer patients experiencing at least one brain metastasis. Earlier detection may significantly improve patient outcomes and overall survival. We investigated machine learning (ML) models for early detection of brain metastases based on diffusion weighted imaging (DWI) radiomics. Methods Longitudinal diffusion imaging from 116 patients previously treated with stereotactic radiosurgery (SRS) for brain metastases were retrospectively analyzed. Clinical contours from 600 metastases were extracted from radiosurgery planning computed tomography, and rigidly registered to corresponding contrast enhanced-T1 and apparent diffusion coefficient (ADC) maps. Contralateral contours located in healthy brain tissue were used as control. The dataset consisted of (I) radiomic features using ADC maps, (II) radiomic feature change calculated using timepoints before the metastasis manifested on contrast enhanced-T1, (III) primary cancer, and (IV) anatomical location. The dataset was divided into training and internal validation sets using an 80/20 split with stratification. Four classification algorithms [Linear Support Vector Machine (SVM), Random Forest (RF), AdaBoost, and XGBoost] underwent supervised classification training, with contours labeled either 'control' or 'metastasis'. Hyperparameters were optimized towards balanced accuracy. Various model metrics (receiver operating characteristic curve area scores, accuracy, recall, and precision) were calculated to gauge performance. Results The radiomic and clinical data set, feature engineering, and ML models developed were able to identify metastases with an accuracy of up to 87.7% on the training set, and 85.8% on an unseen test set. XGBoost and RF showed superior accuracy (XGBoost: 0.877±0.021 and 0.833±0.47, RF: 0.823±0.024 and 0.858±0.045) for training and validation sets, respectively. XGBoost and RF also showed strong area under the receiver operating characteristic curve (AUC) performance on the validation set (0.910±0.037 and 0.922±0.034, respectively). AdaBoost performed slightly lower in all metrics. SVM model generalized poorly with the internal validation set. Important features involved changes in radiomics months before manifesting on contrast enhanced-T1. Conclusions The proposed models using diffusion-based radiomics showed encouraging results in differentiating healthy brain tissue from metastases using clinical imaging data. These findings suggest that longitudinal diffusion imaging and ML may help improve patient care through earlier diagnosis and increased patient monitoring/follow-up. Future work aims to improve model classification metrics, robustness, user-interface, and clinical applicability.
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Affiliation(s)
- Joseph Madamesila
- Department of Physics and Astronomy, University of Calgary, Calgary, Canada
- Department of Medical Physics, Tom Baker Cancer Centre, Alberta Health Services, Calgary, Canada
| | - Ekaterina Tchistiakova
- Department of Physics and Astronomy, University of Calgary, Calgary, Canada
- Department of Medical Physics, Tom Baker Cancer Centre, Alberta Health Services, Calgary, Canada
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Salman Faruqi
- Department of Radiation Oncology, Tom Baker Cancer Center, Alberta Health Services, Calgary, Canada
| | - Subhadip Das
- Division of Radiation Oncology and Developmental Radiotherapeutics, BC Cancer Agency-Victoria, University of British Columbia, Victoria, Canada
| | - Nicolas Ploquin
- Department of Physics and Astronomy, University of Calgary, Calgary, Canada
- Department of Medical Physics, Tom Baker Cancer Centre, Alberta Health Services, Calgary, Canada
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Canada
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Tang VH, Duong STM, Nguyen CDT, Huynh TM, Duc VT, Phan C, Le H, Bui T, Truong SQH. Wavelet radiomics features from multiphase CT images for screening hepatocellular carcinoma: analysis and comparison. Sci Rep 2023; 13:19559. [PMID: 37950031 PMCID: PMC10638447 DOI: 10.1038/s41598-023-46695-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023] Open
Abstract
Early detection of liver malignancy based on medical image analysis plays a crucial role in patient prognosis and personalized treatment. This task, however, is challenging due to several factors, including medical data scarcity and limited training samples. This paper presents a study of three important aspects of radiomics feature from multiphase computed tomography (CT) for classifying hepatocellular carcinoma (HCC) and other focal liver lesions: wavelet-transformed feature extraction, relevant feature selection, and radiomics features-based classification under the inadequate training samples. Our analysis shows that combining radiomics features extracted from the wavelet and original CT domains enhance the classification performance significantly, compared with using those extracted from the wavelet or original domain only. To facilitate the multi-domain and multiphase radiomics feature combination, we introduce a logistic sparsity-based model for feature selection with Bayesian optimization and find that the proposed model yields more discriminative and relevant features than several existing methods, including filter-based, wrapper-based, or other model-based techniques. In addition, we present analysis and performance comparison with several recent deep convolutional neural network (CNN)-based feature models proposed for hepatic lesion diagnosis. The results show that under the inadequate data scenario, the proposed wavelet radiomics feature model produces comparable, if not higher, performance metrics than the CNN-based feature models in terms of area under the curve.
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Affiliation(s)
- Van Ha Tang
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- Le Quy Don Technical University, 236 Hoang Quoc Viet, Hanoi, 11917, Vietnam
| | - Soan T M Duong
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam.
- Le Quy Don Technical University, 236 Hoang Quoc Viet, Hanoi, 11917, Vietnam.
| | - Chanh D Tr Nguyen
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
| | - Thanh M Huynh
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
| | - Vo T Duc
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Chien Phan
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Huyen Le
- University Medical Center Ho Chi Minh City, 215 Hong Bang, Ho Chi Minh City, 12406, Vietnam
| | - Trung Bui
- Adobe Research, San Francisco, CA, 94103, USA
| | - Steven Q H Truong
- VinBrain JSC., 458 Minh Khai, Hanoi, 11619, Vietnam
- VinUniversity, Vinhomes Ocean Park, Hanoi, 12406, Vietnam
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Alharthi AG, Alzahrani SM. Multi-Slice Generation sMRI and fMRI for Autism Spectrum Disorder Diagnosis Using 3D-CNN and Vision Transformers. Brain Sci 2023; 13:1578. [PMID: 38002538 PMCID: PMC10670036 DOI: 10.3390/brainsci13111578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Researchers have explored various potential indicators of ASD, including changes in brain structure and activity, genetics, and immune system abnormalities, but no definitive indicator has been found yet. Therefore, this study aims to investigate ASD indicators using two types of magnetic resonance images (MRI), structural (sMRI) and functional (fMRI), and to address the issue of limited data availability. Transfer learning is a valuable technique when working with limited data, as it utilizes knowledge gained from a pre-trained model in a domain with abundant data. This study proposed the use of four vision transformers namely ConvNeXT, MobileNet, Swin, and ViT using sMRI modalities. The study also investigated the use of a 3D-CNN model with sMRI and fMRI modalities. Our experiments involved different methods of generating data and extracting slices from raw 3D sMRI and 4D fMRI scans along the axial, coronal, and sagittal brain planes. To evaluate our methods, we utilized a standard neuroimaging dataset called NYU from the ABIDE repository to classify ASD subjects from typical control subjects. The performance of our models was evaluated against several baselines including studies that implemented VGG and ResNet transfer learning models. Our experimental results validate the effectiveness of the proposed multi-slice generation with the 3D-CNN and transfer learning methods as they achieved state-of-the-art results. In particular, results from 50-middle slices from the fMRI and 3D-CNN showed a profound promise in ASD classifiability as it obtained a maximum accuracy of 0.8710 and F1-score of 0.8261 when using the mean of 4D images across the axial, coronal, and sagittal. Additionally, the use of the whole slices in fMRI except the beginnings and the ends of brain views helped to reduce irrelevant information and showed good performance of 0.8387 accuracy and 0.7727 F1-score. Lastly, the transfer learning with the ConvNeXt model achieved results higher than other transformers when using 50-middle slices sMRI along the axial, coronal, and sagittal planes.
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Affiliation(s)
| | - Salha M. Alzahrani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia;
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Zhang S, Yin L, Ma L, Sun H. Artificial Intelligence Applications in Glioma With 1p/19q Co-Deletion: A Systematic Review. J Magn Reson Imaging 2023; 58:1338-1352. [PMID: 37083159 DOI: 10.1002/jmri.28737] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/02/2023] [Accepted: 04/03/2023] [Indexed: 04/22/2023] Open
Abstract
As an important genomic marker for oligodendrogliomas, early determination of 1p/19q co-deletion status is critical for guiding therapy and predicting prognosis in patients with glioma. The purpose of this study is to systematically review the literature concerning the magnetic resonance imaging (MRI) with artificial intelligence (AI) methods for predicting 1p/19q co-deletion status in glioma. PubMed, Scopus, Embase, and IEEE Xplore were searched in accordance with the Preferred Reporting Items for systematic reviews and meta-analyses guidelines. Methodological quality of studies was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2. Finally, 28 studies were included in the quantitative analysis. Diagnostic test accuracy reached an area under the ROC curve of 0.71-0.98 were reported in 24 studies. The remaining four studies with no available AUC provided an accuracy of 0.75-0. 89. The included studies varied widely in terms of imaging sequences, input features, and modeling methods. The current review highlighted that integrating MRI with AI technology is a potential tool for determination 1p/19q status pre-operatively and noninvasively, which can possibly help clinical decision-making. However, the reliability and feasibility of this approach still need to be further validated and improved in a real clinical setting. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Simin Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Lijuan Yin
- Department of Pathology, West China Hospital of Sichuan University, Chengdu, China
| | - Lu Ma
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, China
| | - Huaiqiang Sun
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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Zhang J, Wu J, Zhou XS, Shi F, Shen D. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Semin Cancer Biol 2023; 96:11-25. [PMID: 37704183 DOI: 10.1016/j.semcancer.2023.09.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/03/2023] [Accepted: 09/05/2023] [Indexed: 09/15/2023]
Abstract
Breast cancer is a significant global health burden, with increasing morbidity and mortality worldwide. Early screening and accurate diagnosis are crucial for improving prognosis. Radiographic imaging modalities such as digital mammography (DM), digital breast tomosynthesis (DBT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine techniques, are commonly used for breast cancer assessment. And histopathology (HP) serves as the gold standard for confirming malignancy. Artificial intelligence (AI) technologies show great potential for quantitative representation of medical images to effectively assist in segmentation, diagnosis, and prognosis of breast cancer. In this review, we overview the recent advancements of AI technologies for breast cancer, including 1) improving image quality by data augmentation, 2) fast detection and segmentation of breast lesions and diagnosis of malignancy, 3) biological characterization of the cancer such as staging and subtyping by AI-based classification technologies, 4) prediction of clinical outcomes such as metastasis, treatment response, and survival by integrating multi-omics data. Then, we then summarize large-scale databases available to help train robust, generalizable, and reproducible deep learning models. Furthermore, we conclude the challenges faced by AI in real-world applications, including data curating, model interpretability, and practice regulations. Besides, we expect that clinical implementation of AI will provide important guidance for the patient-tailored management.
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Affiliation(s)
- Jiadong Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jiaojiao Wu
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Xiang Sean Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China.
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48
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Hussain S, Lafarga-Osuna Y, Ali M, Naseem U, Ahmed M, Tamez-Peña JG. Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review. BMC Bioinformatics 2023; 24:401. [PMID: 37884877 PMCID: PMC10605943 DOI: 10.1186/s12859-023-05515-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images. OBJECTIVE AND METHODS This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers. RESULTS A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images. CONCLUSION Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.
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Affiliation(s)
- Sadam Hussain
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico.
| | - Yareth Lafarga-Osuna
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Mansoor Ali
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Usman Naseem
- College of Science and Engineering, James Cook University, Cairns, Australia
| | - Masroor Ahmed
- School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
| | - Jose Gerardo Tamez-Peña
- School of Medicine and Health Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico
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49
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Damer A, Chaudry E, Eftekhari D, Benseler SM, Safi F, Aviv RI, Tyrrell PN. Neuroimaging Scoring Tools to Differentiate Inflammatory Central Nervous System Small-Vessel Vasculitis: A Need for Artificial Intelligence/Machine Learning?-A Scoping Review. Tomography 2023; 9:1811-1828. [PMID: 37888736 PMCID: PMC10610796 DOI: 10.3390/tomography9050144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 09/26/2023] [Accepted: 09/29/2023] [Indexed: 10/28/2023] Open
Abstract
Neuroimaging has a key role in identifying small-vessel vasculitis from common diseases it mimics, such as multiple sclerosis. Oftentimes, a multitude of these conditions present similarly, and thus diagnosis is difficult. To date, there is no standardized method to differentiate between these diseases. This review identifies and presents existing scoring tools that could serve as a starting point for integrating artificial intelligence/machine learning (AI/ML) into the clinical decision-making process for these rare diseases. A scoping literature review of EMBASE and MEDLINE included 114 articles to evaluate what criteria exist to diagnose small-vessel vasculitis and common mimics. This paper presents the existing criteria of small-vessel vasculitis conditions and mimics them to guide the future integration of AI/ML algorithms to aid in diagnosing these conditions, which present similarly and non-specifically.
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Affiliation(s)
- Alameen Damer
- Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
- Department of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Emaan Chaudry
- Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
| | - Daniel Eftekhari
- Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
| | - Susanne M. Benseler
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Frozan Safi
- Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
| | - Richard I. Aviv
- Department of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Pascal N. Tyrrell
- Department of Medical Imaging, University of Toronto, Toronto, ON M5T 1W7, Canada
- Institute of Medical Science, Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1X6, Canada
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50
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Mirón-Mombiela R, Ruiz-España S, Moratal D, Borrás C. Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques. Mech Ageing Dev 2023; 215:111860. [PMID: 37666473 DOI: 10.1016/j.mad.2023.111860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/08/2023] [Accepted: 08/30/2023] [Indexed: 09/06/2023]
Abstract
The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participants were subdivided according to frailty phenotype and were followed up for two years. Primary and secondary outcome measures were death and comorbidity, respectively. Forty-three texture features were computed from the rectus femoris and the vastus intermedius muscles using statistical methods. Model performance was evaluated by computing the area under the receiver operating characteristic curve (AUC) while outcome prediction was evaluated using regression analysis. Models developed achieved a moderate to good AUC (0.67 ≤ AUC ≤ 0.79) for categorizing frailty. The stepwise multiple logistic regression analysis demonstrated that they correctly classified 70-87% of the cases. The models were associated with increased comorbidity (0.01 ≤ p ≤ 0.18) and were predictive of death for pre-frail and frail participants (0.001 ≤ p ≤ 0.016). In conclusion, texture analysis can be useful to identify frailty and assess risk prediction (i.e. mortality) using texture features extracted from muscle ultrasound images in combination with a machine learning approach.
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Affiliation(s)
- Rebeca Mirón-Mombiela
- Department of Physiology, Universitat de València/INCLIVA, Avda. Blasco Ibáñez, 15, 46010 Valencia, Spain; Hospital General Universitario de Valencia (HGUV), Valencia, Spain; Herlev og Gentofte Hospital, Herlev, Denmark.
| | - Silvia Ruiz-España
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain.
| | - David Moratal
- Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain.
| | - Consuelo Borrás
- Department of Physiology, Universitat de València/INCLIVA, Avda. Blasco Ibáñez, 15, 46010 Valencia, Spain; INCLIVA Health Research Institute, Av/ de Menéndez y Pelayo, 4, 46010 Valencia, Spain; Center for Biomedical Network Research on Frailty and Healthy Aging (CIBERFES), CIBER-ISCIII, Valencia, Spain.
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