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Zsarnoczay E, Rapaka S, Schoepf UJ, Gnasso C, Vecsey-Nagy M, Todoran TM, Hagar MT, Kravchenko D, Tremamunno G, Griffith JP, Fink N, Derrick S, Bowman M, Sam H, Tiller M, Godoy K, Condrea F, Sharma P, O'Doherty J, Maurovich-Horvat P, Emrich T, Varga-Szemes A. Accuracy of a deep neural network for automated pulmonary embolism detection on dedicated CT pulmonary angiograms. Eur J Radiol 2025; 187:112077. [PMID: 40187199 DOI: 10.1016/j.ejrad.2025.112077] [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: 12/22/2024] [Revised: 03/11/2025] [Accepted: 03/26/2025] [Indexed: 04/07/2025]
Abstract
PURPOSE To assess the performance of a Deep Neural Network (DNN)-based prototype algorithm for automated PE detection on CTPA scans. METHODS Patients who had previously undergone CTPA with three different systems (SOMATOM Force, go.Top, and Definition AS; Siemens Healthineers, Forchheim, Germany) because of suspected PE from September 2022 to January 2023 were retrospectively enrolled in this study (n = 1,000, 58.8 % women). For detailed evaluation, all PE were divided into three location-based subgroups: central arteries, lobar branches, and peripheral regions. Clinical reports served as ground truth. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were determined to evaluate the performance of DNN-based PE detection. RESULTS Cases were excluded due to incomplete data (n = 32), inconclusive report (n = 17), insufficient contrast detected in the pulmonary trunk (n = 40), or failure of the preprocessing algorithms (n = 8). Therefore, the final cohort included 903 cases with a PE prevalence of 12 % (n = 110). The model achieved a sensitivity, specificity, PPV, and NPV of 84.6, 95.1, 70.5, and 97.8 %, respectively, and delivered an overall accuracy of 93.8 %. Among the false positive cases (n = 39), common sources of error included lung masses, pneumonia, and contrast flow artifacts. Common sources of false negatives (n = 17) included chronic and subsegmental PEs. CONCLUSION The proposed DNN-based algorithm provides excellent performance for the detection of PE, suggesting its potential utility to support radiologists in clinical reading and exam prioritization.
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Affiliation(s)
- Emese Zsarnoczay
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA; Radiology Department, Medical Imaging Centre, Semmelweis University, Korányi Sándor utca 2, 1083 Budapest, Hungary.
| | - Saikiran Rapaka
- Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540, USA.
| | - U Joseph Schoepf
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA.
| | - Chiara Gnasso
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA; Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132 Milan, Italy.
| | - Milan Vecsey-Nagy
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA; Heart and Vascular Center, Semmelweis University, Gaál József út 9, 1122 Budapest, Hungary.
| | - Thomas M Todoran
- Division of Cardiology, Department of Medicine, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA.
| | - Muhammad Taha Hagar
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA; Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Hugstetter Straße 55, Freiburg im Breisgau 79106, Germany.
| | - Dmitrij Kravchenko
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA; Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany.
| | - Giuseppe Tremamunno
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA; Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome - Radiology Unit - Sant'Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy.
| | - Joseph Parkwood Griffith
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA.
| | - Nicola Fink
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA; Department of Radiology, University Hospital, LMU Munich, Marchioninistraße 15, 81377 Munich, Germany.
| | - Sydney Derrick
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA.
| | - Meredith Bowman
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA.
| | - Henry Sam
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA.
| | - Mikayla Tiller
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA.
| | - Kathleen Godoy
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA.
| | - Florin Condrea
- Siemens Healthineers, Nine, Bulevardul Gării 13A, Brașov 500227, Romania.
| | - Puneet Sharma
- Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540, USA.
| | - Jim O'Doherty
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA; Siemens Medical Solutions, 40 Liberty Blvd, Malvern, PA 19355, USA.
| | - Pal Maurovich-Horvat
- Radiology Department, Medical Imaging Centre, Semmelweis University, Korányi Sándor utca 2, 1083 Budapest, Hungary.
| | - Tilman Emrich
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA; Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg-University, Langenbeckstraße 1, Mainz 55131, Germany; German Centre for Cardiovascular Research, Mainz, Germany.
| | - Akos Varga-Szemes
- Department of Radiology and Radiological Science, Medical University of South Carolina, 25 Courtenay Drive, Charleston, SC 29425, USA.
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Lanza E, Ammirabile A, Francone M. Meta-analysis of AI-based pulmonary embolism detection: How reliable are deep learning models? Comput Biol Med 2025; 193:110402. [PMID: 40412084 DOI: 10.1016/j.compbiomed.2025.110402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2025] [Revised: 04/18/2025] [Accepted: 05/17/2025] [Indexed: 05/27/2025]
Abstract
RATIONALE AND OBJECTIVES Deep learning (DL)-based methods show promise in detecting pulmonary embolism (PE) on CT pulmonary angiography (CTPA), potentially improving diagnostic accuracy and workflow efficiency. This meta-analysis aimed to (1) determine pooled performance estimates of DL algorithms for PE detection; and (2) compare the diagnostic efficacy of convolutional neural network (CNN)- versus U-Net-based architectures. MATERIALS AND METHODS Following PRISMA guidelines, we searched PubMed and EMBASE through April 15, 2025 for English-language studies (2010-2025) reporting DL models for PE detection with extractable 2 × 2 data or performance metrics. True/false positives and negatives were reconstructed when necessary under an assumed 50 % PE prevalence (with 0.5 continuity correction). We approximated AUROC as the mean of sensitivity and specificity if not directly reported. Sensitivity, specificity, accuracy, PPV and NPV were pooled using a DerSimonian-Laird random-effects model with Freeman-Tukey transformation; AUROC values were combined via a fixed-effect inverse-variance approach. Heterogeneity was assessed by Cochran's Q and I2. Subgroup analyses contrasted CNN versus U-Net models. RESULTS Twenty-four studies (n = 22,984 patients) met inclusion criteria. Pooled estimates were: AUROC 0.895 (95 % CI: 0.874-0.917), sensitivity 0.894 (0.856-0.923), specificity 0.871 (0.831-0.903), accuracy 0.857 (0.833-0.882), PPV 0.832 (0.794-0.869) and NPV 0.902 (0.874-0.929). Between-study heterogeneity was high (I2 ≈ 97 % for sensitivity/specificity). U-Net models exhibited higher sensitivity (0.899 vs 0.893) and CNN models higher specificity (0.926 vs 0.900); subgroup Q-tests confirmed significant differences for both sensitivity (p = 0.0002) and specificity (p < 0.001). CONCLUSIONS DL algorithms demonstrate high diagnostic accuracy for PE detection on CTPA, with complementary strengths: U-Net architectures excel in true-positive identification, whereas CNNs yield fewer false positives. However, marked heterogeneity underscores the need for standardized, prospective validation before routine clinical implementation.
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Affiliation(s)
- Ezio Lanza
- Humanitas University, Department of Biomedical Sciences, via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy; IRCCS Humanitas Research Hospital, Radiology Department, via Manzoni 56, Rozzano, 20089, Milan, Italy.
| | - Angela Ammirabile
- Humanitas University, Department of Biomedical Sciences, via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy; IRCCS Humanitas Research Hospital, Radiology Department, via Manzoni 56, Rozzano, 20089, Milan, Italy
| | - Marco Francone
- Humanitas University, Department of Biomedical Sciences, via Rita Levi Montalcini 4, Pieve Emanuele, 20072, Milan, Italy; IRCCS Humanitas Research Hospital, Radiology Department, via Manzoni 56, Rozzano, 20089, Milan, Italy
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Liu YK, Cisneros J, Nair G, Stevens C, Castillo R, Vinogradskiy Y, Castillo E. Perfusion estimation from dynamic non-contrast computed tomography using self-supervised learning and a physics-inspired U-net transformer architecture. Int J Comput Assist Radiol Surg 2025; 20:959-970. [PMID: 39832070 PMCID: PMC12055896 DOI: 10.1007/s11548-025-03323-2] [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: 07/01/2024] [Accepted: 01/07/2025] [Indexed: 01/22/2025]
Abstract
PURPOSE Pulmonary perfusion imaging is a key lung health indicator with clinical utility as a diagnostic and treatment planning tool. However, current nuclear medicine modalities face challenges like low spatial resolution and long acquisition times which limit clinical utility to non-emergency settings and often placing extra financial burden on the patient. This study introduces a novel deep learning approach to predict perfusion imaging from non-contrast inhale and exhale computed tomography scans (IE-CT). METHODS We developed a U-Net Transformer architecture modified for Siamese IE-CT inputs, integrating insights from physical models and utilizing a self-supervised learning strategy tailored for lung function prediction. We aggregated 523 IE-CT images from nine different 4DCT imaging datasets for self-supervised training, aiming to learn a low-dimensional IE-CT feature space by reconstructing image volumes from random data augmentations. Supervised training for perfusion prediction used this feature space and transfer learning on a cohort of 44 patients who had both IE-CT and single-photon emission CT (SPECT/CT) perfusion scans. RESULTS Testing with random bootstrapping, we estimated the mean and standard deviation of the spatial Spearman correlation between our predictions and the ground truth (SPECT perfusion) to be 0.742 ± 0.037, with a mean median correlation of 0.792 ± 0.036. These results represent a new state-of-the-art accuracy for predicting perfusion imaging from non-contrast CT. CONCLUSION Our approach combines low-dimensional feature representations of both inhale and exhale images into a deep learning model, aligning with previous physical modeling methods for characterizing perfusion from IE-CT. This likely contributes to the high spatial correlation with ground truth. With further development, our method could provide faster and more accurate lung function imaging, potentially expanding its clinical applications beyond what is currently possible with nuclear medicine.
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Affiliation(s)
- Yi-Kuan Liu
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Jorge Cisneros
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Girish Nair
- Division of Pulmonary and Critical Care, Beaumont Health, Royal Oak, MI, USA
| | - Craig Stevens
- Division of Radiation Oncology, Beaumont Health, Royal Oak, MI, USA
| | - Richard Castillo
- Division of Radiation Oncology, Emory University, Atlanta, GA, USA
| | | | - Edward Castillo
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
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Sachpekidis C, Goldschmidt H, Edenbrandt L, Dimitrakopoulou-Strauss A. Radiomics and Artificial Intelligence Landscape for [ 18F]FDG PET/CT in Multiple Myeloma. Semin Nucl Med 2025; 55:387-395. [PMID: 39674756 DOI: 10.1053/j.semnuclmed.2024.11.005] [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: 11/18/2024] [Accepted: 11/22/2024] [Indexed: 12/16/2024]
Abstract
[18F]FDG PET/CT is a powerful imaging modality of high performance in multiple myeloma (MM) and is considered the appropriate method for assessing treatment response in this disease. On the other hand, due to the heterogeneous and sometimes complex patterns of bone marrow infiltration in MM, the interpretation of PET/CT can be particularly challenging, hampering interobserver reproducibility and limiting the diagnostic and prognostic ability of the modality. Although many approaches have been developed to address the issue of standardization, none can yet be considered a standard method for interpretation or objective quantification of PET/CT. Therefore, advanced diagnostic quantification approaches are needed to support and potentially guide the management of MM. In recent years, radiomics has emerged as an innovative method for high-throughput mining of image-derived features for clinical decision making, which may be particularly helpful in oncology. In addition, machine learning and deep learning, both subfields of artificial intelligence (AI) closely related to the radiomics process, have been increasingly applied to automated image analysis, offering new possibilities for a standardized evaluation of imaging modalities such as CT, PET/CT and MRI in oncology. In line with this, the initial but steadily growing literature on the application of radiomics and AI-based methods in the field of [18F]FDG PET/CT in MM has already yielded encouraging results, offering a potentially reliable tool towards optimization and standardization of interpretation in this disease. The main results of these studies are presented in this review.
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Affiliation(s)
- Christos Sachpekidis
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Hartmut Goldschmidt
- Internal Medicine V, Hematology, Oncology and Rheumatology, German-Speaking Myeloma Multicenter Group (GMMG), Heidelberg University Hospital, Heidelberg, Germany
| | - Lars Edenbrandt
- Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden; Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Shin Y, Lee M, Lee Y, Kim K, Kim T. Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care-Innovations, Limitations, and Future Directions. Life (Basel) 2025; 15:654. [PMID: 40283208 PMCID: PMC12028931 DOI: 10.3390/life15040654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2025] [Revised: 04/09/2025] [Accepted: 04/14/2025] [Indexed: 04/29/2025] Open
Abstract
Artificial intelligence is rapidly transforming quality assurance in healthcare, driving advancements in diagnostics, surgery, and patient care. This review presents a comprehensive analysis of artificial intelligence integration-particularly convolutional and recurrent neural networks-across key clinical domains, significantly enhancing diagnostic accuracy, surgical performance, and pathology evaluation. Artificial intelligence-based approaches have demonstrated clear superiority over conventional methods: convolutional neural networks achieved 91.56% accuracy in scanner fault detection, surpassing manual inspections; endoscopic lesion detection sensitivity rose from 2.3% to 6.1% with artificial intelligence assistance; and gastric cancer invasion depth classification reached 89.16% accuracy, outperforming human endoscopists by 17.25%. In pathology, artificial intelligence achieved 93.2% accuracy in identifying out-of-focus regions and an F1 score of 0.94 in lymphocyte quantification, promoting faster and more reliable diagnostics. Similarly, artificial intelligence improved surgical workflow recognition with over 81% accuracy and exceeded 95% accuracy in skill assessment classification. Beyond traditional diagnostics and surgical support, AI-powered wearable sensors, drug delivery systems, and biointegrated devices are advancing personalized treatment by optimizing physiological monitoring, automating care protocols, and enhancing therapeutic precision. Despite these achievements, challenges remain in areas such as data standardization, ethical governance, and model generalizability. Overall, the findings underscore artificial intelligence's potential to outperform traditional techniques across multiple parameters, emphasizing the need for continued development, rigorous clinical validation, and interdisciplinary collaboration to fully realize its role in precision medicine and patient safety.
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Affiliation(s)
- Yoojin Shin
- College of Medicine, The Catholic University of Korea, 222 Banpo-Daero, Seocho-gu, Seoul 06591, Republic of Korea; (Y.S.); (M.L.); (Y.L.)
| | - Mingyu Lee
- College of Medicine, The Catholic University of Korea, 222 Banpo-Daero, Seocho-gu, Seoul 06591, Republic of Korea; (Y.S.); (M.L.); (Y.L.)
| | - Yoonji Lee
- College of Medicine, The Catholic University of Korea, 222 Banpo-Daero, Seocho-gu, Seoul 06591, Republic of Korea; (Y.S.); (M.L.); (Y.L.)
| | - Kyuri Kim
- College of Medicine, Ewha Womans University, 25 Magokdong-ro 2-gil, Gangseo-gu, Seoul 07804, Republic of Korea;
| | - Taejung Kim
- Department of Hospital Pathology, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 10, 63-ro, Yeongdeungpo-gu, Seoul 07345, Republic of Korea
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Zhong Z, Zhang H, Fayad FH, Lancaster AC, Sollee J, Kulkarni S, Lin CT, Li J, Gao X, Collins S, Greineder CF, Ahn SH, Bai HX, Jiao Z, Atalay MK. Pulmonary Embolism Survival Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data. J Thorac Imaging 2025:00005382-990000000-00172. [PMID: 40200808 DOI: 10.1097/rti.0000000000000831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2025]
Abstract
PURPOSE Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using computed tomography pulmonary angiography (CTPA), clinical data, and PE Severity Index (PESI) scores to predict PE survival. MATERIALS AND METHODS In total, 918 patients (median age 64 y, range 13 to 99 y, 48% male) with 3978 CTPAs were identified via retrospective review across 3 institutions. To predict survival, an AI model was used to extract disease-related imaging features from CTPAs. Imaging features and clinical variables were then incorporated into independent DL models to predict survival outcomes. Cross-modal fusion CoxPH models were used to develop multimodal models from combinations of DL models and calculated PESI scores. Five multimodal models were developed as follows: (1) using CTPA imaging features only, (2) using clinical variables only, (3) using both CTPA and clinical variables, (4) using CTPA and PESI score, and (5) using CTPA, clinical variables, and PESI score. Performance was evaluated using the concordance index (c-index). Kaplan-Meier analysis was performed to stratify patients into high-risk and low-risk groups. Additional factor-risk analysis was conducted to account for right ventricular (RV) dysfunction. RESULTS For both data sets, the multimodal models incorporating CTPA features, clinical variables, and PESI score achieved higher c-indices than PESI alone. Following the stratification of patients into high-risk and low-risk groups by models, survival outcomes differed significantly (both P<0.001). A strong correlation was found between high-risk grouping and RV dysfunction. CONCLUSIONS Multiomic DL models incorporating CTPA features, clinical data, and PESI achieved higher c-indices than PESI alone for PE survival prediction.
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Affiliation(s)
- Zhusi Zhong
- Department of Diagnostic Radiology, Rhode Island Hospital
- Warren Alpert Medical School of Brown University, Providence, RI
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Helen Zhang
- Department of Diagnostic Radiology, Rhode Island Hospital
- Warren Alpert Medical School of Brown University, Providence, RI
| | - Fayez H Fayad
- Department of Diagnostic Radiology, Rhode Island Hospital
- Warren Alpert Medical School of Brown University, Providence, RI
| | - Andrew C Lancaster
- Johns Hopkins University School of Medicine
- Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - John Sollee
- Department of Diagnostic Radiology, Rhode Island Hospital
- Warren Alpert Medical School of Brown University, Providence, RI
| | - Shreyas Kulkarni
- Department of Diagnostic Radiology, Rhode Island Hospital
- Warren Alpert Medical School of Brown University, Providence, RI
| | - Cheng Ting Lin
- Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Jie Li
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Xinbo Gao
- School of Electronic Engineering, Xidian University, Xi'an, China
| | - Scott Collins
- Department of Diagnostic Radiology, Rhode Island Hospital
- Warren Alpert Medical School of Brown University, Providence, RI
| | - Colin F Greineder
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI
| | - Sun H Ahn
- Department of Diagnostic Radiology, Rhode Island Hospital
- Warren Alpert Medical School of Brown University, Providence, RI
| | - Harrison X Bai
- Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Zhicheng Jiao
- Department of Diagnostic Radiology, Rhode Island Hospital
- Warren Alpert Medical School of Brown University, Providence, RI
| | - Michael K Atalay
- Department of Diagnostic Radiology, Rhode Island Hospital
- Warren Alpert Medical School of Brown University, Providence, RI
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Lim WH, Kim H. Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review. Tuberc Respir Dis (Seoul) 2025; 88:278-291. [PMID: 39689720 PMCID: PMC12010722 DOI: 10.4046/trd.2024.0062] [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: 05/02/2024] [Revised: 09/02/2024] [Accepted: 12/11/2024] [Indexed: 12/19/2024] Open
Abstract
Thoracic radiology has emerged as a primary field in which artificial intelligence (AI) is extensively researched. Recent advancements highlight the potential to enhance radiologists' performance through AI. AI aids in detecting and classifying abnormalities, and in quantifying both normal and abnormal anatomical structures. Additionally, it facilitates prognostication by leveraging these quantitative values. This review article will discuss the recent achievements of AI in thoracic radiology, focusing primarily on deep learning, and explore the current limitations and future directions of this cutting-edge technique.
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Affiliation(s)
- Woo Hyeon Lim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyungjin Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
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Naser AM, Vyas R, Morgan AA, Kalaiger AM, Kharawala A, Nagraj S, Agarwal R, Maliha M, Mangeshkar S, Singh N, Satish V, Mathai S, Palaiodimos L, Faillace RT. Role of Artificial Intelligence in the Diagnosis and Management of Pulmonary Embolism: A Comprehensive Review. Diagnostics (Basel) 2025; 15:889. [PMID: 40218239 PMCID: PMC11988985 DOI: 10.3390/diagnostics15070889] [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: 12/22/2024] [Revised: 03/09/2025] [Accepted: 03/27/2025] [Indexed: 04/14/2025] Open
Abstract
Pulmonary embolism (PE) remains a critical condition with significant mortality and morbidity, necessitating timely detection and intervention to improve patient outcomes. This review examines the evolving role of artificial intelligence (AI) in PE management. Two primary AI-driven models that are currently being explored are deep convolutional neural networks (DCNNs) for enhanced image-based detection and natural language processing (NLP) for improved risk stratification using electronic health records. A major advancement in this field was the FDA approval of the Aidoc© AI model, which has demonstrated high specificity and negative predictive value in PE diagnosis from imaging scans. Additionally, AI is being explored for optimizing anticoagulation strategies and predicting PE recurrence risk. While further large-scale studies are needed to fully establish AI's role in clinical practice, its integration holds significant potential to enhance diagnostic accuracy and overall patient management.
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Affiliation(s)
- Ahmad Moayad Naser
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA; (A.M.N.); (R.V.); (A.A.M.); (R.A.); (M.M.); (S.M.); (N.S.); (V.S.); (L.P.); (R.T.F.)
| | - Rhea Vyas
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA; (A.M.N.); (R.V.); (A.A.M.); (R.A.); (M.M.); (S.M.); (N.S.); (V.S.); (L.P.); (R.T.F.)
| | - Ahmed Ashraf Morgan
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA; (A.M.N.); (R.V.); (A.A.M.); (R.A.); (M.M.); (S.M.); (N.S.); (V.S.); (L.P.); (R.T.F.)
| | | | - Amrin Kharawala
- Department of Cardiology, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Sanjana Nagraj
- Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY 10461, USA; (S.N.)
| | - Raksheeth Agarwal
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA; (A.M.N.); (R.V.); (A.A.M.); (R.A.); (M.M.); (S.M.); (N.S.); (V.S.); (L.P.); (R.T.F.)
| | - Maisha Maliha
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA; (A.M.N.); (R.V.); (A.A.M.); (R.A.); (M.M.); (S.M.); (N.S.); (V.S.); (L.P.); (R.T.F.)
| | - Shaunak Mangeshkar
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA; (A.M.N.); (R.V.); (A.A.M.); (R.A.); (M.M.); (S.M.); (N.S.); (V.S.); (L.P.); (R.T.F.)
| | - Nikita Singh
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA; (A.M.N.); (R.V.); (A.A.M.); (R.A.); (M.M.); (S.M.); (N.S.); (V.S.); (L.P.); (R.T.F.)
| | - Vikyath Satish
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA; (A.M.N.); (R.V.); (A.A.M.); (R.A.); (M.M.); (S.M.); (N.S.); (V.S.); (L.P.); (R.T.F.)
| | - Sheetal Mathai
- Department of Cardiology, Montefiore Medical Center, Albert Einstein College of Medicine, New York, NY 10461, USA; (S.N.)
| | - Leonidas Palaiodimos
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA; (A.M.N.); (R.V.); (A.A.M.); (R.A.); (M.M.); (S.M.); (N.S.); (V.S.); (L.P.); (R.T.F.)
| | - Robert T. Faillace
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, New York, NY 10461, USA; (A.M.N.); (R.V.); (A.A.M.); (R.A.); (M.M.); (S.M.); (N.S.); (V.S.); (L.P.); (R.T.F.)
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Song J, Hwang EJ, Yoon SH, Park CM, Goo JM. Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases. Invest Radiol 2025:00004424-990000000-00308. [PMID: 40106831 DOI: 10.1097/rli.0000000000001179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2025]
Abstract
ABSTRACT Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This review summarizes key developments in imaging modalities, computational tools, and clinical applications, highlighting major breakthroughs in thoracic diseases-lung cancer, pulmonary nodules, interstitial lung disease (ILD), chronic obstructive pulmonary disease (COPD), COVID-19 pneumonia, and pulmonary embolism-and outlining future directions.Artificial intelligence (AI)-driven computer-aided detection systems and radiomic analyses have notably improved the detection and classification of pulmonary nodules, while photon-counting detector CT (PCD-CT) and low-field MRI offer enhanced resolution or radiation-free strategies. For lung cancer, CT texture analysis and perfusion imaging refine prognostication and therapy planning. ILD assessment benefits from automated diagnostic tools and innovative imaging techniques, such as PCD-CT and functional MRI, which reduce the need for invasive diagnostic procedures while improving accuracy. In COPD, dual-energy CT-based ventilation/perfusion assessment and dark-field radiography enable earlier detection and staging of emphysema, complemented by deep learning approaches for improved quantification. COVID-19 research has underscored the clinical utility of chest CT, radiographs, and AI-based algorithms for rapid triage, disease severity evaluation, and follow-up. Furthermore, tuberculosis remains a significant global health concern, highlighting the importance of AI-assisted chest radiography for early detection and management. Meanwhile, advances in CT pulmonary angiography, including dual-energy reconstructions, allow more sensitive detection of pulmonary emboli.Collectively, these innovations demonstrate the power of merging novel imaging technologies, quantitative functional analysis, and AI-driven tools to transform thoracic disease management. Ongoing progress promises more precise and personalized diagnostic and therapeutic strategies for diverse thoracic diseases.
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Affiliation(s)
- Jiyoung Song
- From the Department of Radiology, Seoul National University Hospital and College of Medicine, Seoul, Korea (J.S., E.J.H., S.H.Y., C.M.P., J.M.G.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (E.J.H., S.H.Y., C.M.P., J.M.G.); Institute of Medical and Biological Engineering, Seoul National University Medical Research Center, Seoul, Korea (C.M.P.); and Seoul National University Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea (J.M.G.)
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10
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Li L, Peng M, Zou Y, Li Y, Qiao P. The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection. Front Med (Lausanne) 2025; 12:1514931. [PMID: 40177281 PMCID: PMC11961422 DOI: 10.3389/fmed.2025.1514931] [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: 10/21/2024] [Accepted: 02/28/2025] [Indexed: 04/05/2025] Open
Abstract
Computed tomography pulmonary angiography (CTPA) is an essential diagnostic tool for identifying pulmonary embolism (PE). The integration of AI has significantly advanced CTPA-based PE detection, enhancing diagnostic accuracy and efficiency. This review investigates the growing role of AI in the diagnosis of pulmonary embolism using CTPA imaging. The review examines the capabilities of AI algorithms, particularly deep learning models, in analyzing CTPA images for PE detection. It assesses their sensitivity and specificity compared to human radiologists. AI systems, using large datasets and complex neural networks, demonstrate remarkable proficiency in identifying subtle signs of PE, aiding clinicians in timely and accurate diagnosis. In addition, AI-powered CTPA analysis shows promise in risk stratification, prognosis prediction, and treatment optimization for PE patients. Automated image interpretation and quantitative analysis facilitate rapid triage of suspected cases, enabling prompt intervention and reducing diagnostic delays. Despite these advancements, several limitations remain, including algorithm bias, interpretability issues, and the necessity for rigorous validation, which hinder widespread adoption in clinical practice. Furthermore, integrating AI into existing healthcare systems requires careful consideration of regulatory, ethical, and legal implications. In conclusion, AI-driven CTPA-based PE detection presents unprecedented opportunities to enhance diagnostic precision and efficiency. However, addressing the associated limitations is critical for safe and effective implementation in routine clinical practice. Successful utilization of AI in revolutionizing PE care necessitates close collaboration among researchers, medical professionals, and regulatory organizations.
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Affiliation(s)
- Lin Li
- Department of Radiology, Yantaishan Hospital, Yantai, China
| | - Min Peng
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
| | - Yifang Zou
- Department of Equipment, Yantaishan Hospital, Yantai, China
| | - Yunxin Li
- Department of Radiology, Yantaishan Hospital, Yantai, China
| | - Peng Qiao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, China
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11
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Fan JC, Luan H, Qiao Y, Li Y, Ren Y. Detection and segmentation of pulmonary embolism in 3D CT pulmonary angiography using a threshold adjustment segmentation network. Sci Rep 2025; 15:7263. [PMID: 40025153 PMCID: PMC11873198 DOI: 10.1038/s41598-025-91807-1] [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: 10/20/2024] [Accepted: 02/24/2025] [Indexed: 03/04/2025] Open
Abstract
Pulmonary embolism is a life-threatening condition where early diagnosis and precise localization are crucial for improving patient outcomes. While CT pulmonary angiography (CTPA) is the primary method for detecting pulmonary embolism, existing segmentation algorithms struggle to effectively distinguish thrombi from vascular structures in complex 3D CTPA images, often leading to both false positives and false negatives. To address these challenges, the Threshold Adjustment Segmentation Network (TSNet) is proposed to enhance segmentation performance in 3D CTPA images. TSNet incorporates two core modules: the Threshold Adjustment Module (TAD) and the Geometric-Topological Axial Feature Module (GT-AFM). TAD utilizes logarithmic scaling, adaptive adjustments, and nonlinear transformations to optimize the probability distributions of thrombi and vessels, reducing false positives while improving the sensitivity of thrombus detection. GT-AFM integrates geometric features and topological information to enhance the recognition of complex vascular and thrombotic structures, improving spatial feature processing. Experimental results show that TSNet achieves a sensitivity of 0.761 and a false positives per scan of 1.273 at ε = 0 mm. With an increased tolerance of ε = 5 mm, sensitivity improves to 0.878 and false positives per scan decreases to 0.515, significantly reducing false positives. These results indicate that TSNet demonstrates superior segmentation performance under various tolerance levels, showing robustness and a well-balanced trade-off between sensitivity and false positives, making it highly promising for clinical applications.
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Affiliation(s)
- Jian-Cong Fan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong Province, PR China
- Provincial Key Laboratory for Information Technology of Wisdom Mining of Shandong Province, Shandong University of Science and Technology, Qingdao, China
| | - Haoyang Luan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong Province, PR China
- Provincial Key Laboratory for Information Technology of Wisdom Mining of Shandong Province, Shandong University of Science and Technology, Qingdao, China
| | - Yaqian Qiao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Yang Li
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong Province, PR China.
- Provincial Key Laboratory for Information Technology of Wisdom Mining of Shandong Province, Shandong University of Science and Technology, Qingdao, China.
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
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12
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Kabir MM, Rahman A, Hasan MN, Mridha MF. Computer vision algorithms in healthcare: Recent advancements and future challenges. Comput Biol Med 2025; 185:109531. [PMID: 39675214 DOI: 10.1016/j.compbiomed.2024.109531] [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: 11/01/2023] [Revised: 10/05/2024] [Accepted: 12/03/2024] [Indexed: 12/17/2024]
Abstract
Computer vision has emerged as a promising technology with numerous applications in healthcare. This systematic review provides an overview of advancements and challenges associated with computer vision in healthcare. The review highlights the application areas where computer vision has made significant strides, including medical imaging, surgical assistance, remote patient monitoring, and telehealth. Additionally, it addresses the challenges related to data quality, privacy, model interpretability, and integration with existing healthcare systems. Ethical and legal considerations, such as patient consent and algorithmic bias, are also discussed. The review concludes by identifying future directions and opportunities for research, emphasizing the potential impact of computer vision on healthcare delivery and outcomes. Overall, this systematic review underscores the importance of understanding both the advancements and challenges in computer vision to facilitate its responsible implementation in healthcare.
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Affiliation(s)
- Md Mohsin Kabir
- School of Innovation, Design and Engineering, Mälardalens University, Västerås, 722 20, Sweden.
| | - Ashifur Rahman
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur-2, Dhaka, 1216, Bangladesh.
| | - Md Nahid Hasan
- Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, United States.
| | - M F Mridha
- Department of Computer Science, American International University-Bangladesh, Dhaka, 1229, Dhaka, Bangladesh.
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13
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Wang D, Chen R, Wang W, Yang Y, Yu Y, Liu L, Yang F, Cui S. Prediction of short-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images. J Thromb Thrombolysis 2025; 58:331-339. [PMID: 39342072 DOI: 10.1007/s11239-024-03044-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/09/2024] [Indexed: 10/01/2024]
Abstract
To explore the predictive value of traditional machine learning (ML) and deep learning (DL) algorithms based on computed tomography pulmonary angiography (CTPA) images for short-term adverse outcomes in patients with acute pulmonary embolism (APE). This retrospective study enrolled 132 patients with APE confirmed by CTPA. Thrombus segmentation and texture feature extraction was performed using 3D-Slicer software. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature dimensionality reduction and selection, with optimal λ values determined using leave-one-fold cross-validation to identify texture features with non-zero coefficients. ML models (logistic regression, random forest, decision tree, support vector machine) and DL models (ResNet 50 and Vgg 19) were used to construct the prediction models. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). The cohort included 84 patients in the good prognosis group and 48 patients in the poor prognosis group. Univariate and multivariate logistic regression analyses showed that diabetes, RV/LV ≥ 1.0, and Qanadli index form independent risk factors predicting poor prognosis in patients with APE(P < 0.05). A total of 750 texture features were extracted, with 4 key features identified through screening. There was a weak positive correlation between texture features and clinical parameters. ROC curves analysis demonstrated AUC values of 0.85 (0.78-0.92), 0.76 (0.67-0.84), and 0.89 (0.83-0.95) for the clinical, texture feature, and combined models, respectively. In the ML models, the random forest model achieved the highest AUC (0.85), and the support vector machine model achieved the lowest AUC (0.62). And the AUCs for the DL models (ResNet 50 and Vgg 19) were 0.91 (95%CI: 0.90-0.92) and 0.94(95%CI: 0.93-0.95), respectively. Vgg 19 model demonstrated exceptional precision (0.93), recall (0.76), specificity (0.95) and F1 score (0.84). Both ML and DL models based on thrombus texture features from CTPA images demonstrated higher predictive efficacy for short-term adverse outcomes in patients with APE, especially the random forest and Vgg 19 models, potentially assisting clinical management in timely interventions to improve patient prognosis.
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Affiliation(s)
- Dawei Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, 075000, China
| | - Rong Chen
- Hebei North University, Zhangjiakou, Hebei, 075000, China
| | - Wenjiang Wang
- Hebei North University, Zhangjiakou, Hebei, 075000, China
| | - Yue Yang
- Hebei North University, Zhangjiakou, Hebei, 075000, China
| | - Yaxi Yu
- Hebei North University, Zhangjiakou, Hebei, 075000, China
| | - Lan Liu
- Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Zhangjiakou, Hebei, 075000, China
| | - Fei Yang
- Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Zhangjiakou, Hebei, 075000, China.
| | - Shujun Cui
- Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Zhangjiakou, Hebei, 075000, China
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14
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Li J, Shi H, Chen W, Liu N, Hwang KS. Semi-Supervised Detection Model Based on Adaptive Ensemble Learning for Medical Images. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025; 36:237-248. [PMID: 37339032 DOI: 10.1109/tnnls.2023.3282809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscopes. Moreover, works relying on supervised learning are powerless in the case of inadequate labeled samples. Therefore, for end-to-end medical image detection with overcritical efficiency and accuracy in endoscope detection, an ensemble-learning-based model with a semi-supervised mechanism is developed in this work. To gain a more accurate result through multiple detection models, we propose a new ensemble mechanism, termed alternative adaptive boosting method (Al-Adaboost), combining the decision-making of two hierarchical models. Specifically, the proposal consists of two modules. One is a local region proposal model with attentive temporal-spatial pathways for bounding box regression and classification, and the other one is a recurrent attention model (RAM) to provide more precise inferences for further classification according to the regression result. The proposal Al-Adaboost will adjust the weights of labeled samples and the two classifiers adaptively, and the nonlabel samples are assigned pseudolabels by our model. We investigate the performance of Al-Adaboost on both the colonoscopy and laryngoscopy data coming from CVC-ClinicDB and the affiliated hospital of Kaohsiung Medical University. The experimental results prove the feasibility and superiority of our model.
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15
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Mohanarajan M, Salunke PP, Arif A, Iglesias Gonzalez PM, Ospina D, Benavides DS, Amudha C, Raman KK, Siddiqui HF. Advancements in Machine Learning and Artificial Intelligence in the Radiological Detection of Pulmonary Embolism. Cureus 2025; 17:e78217. [PMID: 40026993 PMCID: PMC11872007 DOI: 10.7759/cureus.78217] [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] [Accepted: 01/29/2025] [Indexed: 03/05/2025] Open
Abstract
Pulmonary embolism (PE) is a clinically challenging diagnosis that varies from silent to life-threatening symptoms. Timely diagnosis of the condition is subject to clinical assessment, D-dimer testing and radiological imaging. Computed tomography pulmonary angiogram (CTPA) is considered the gold standard imaging modality, although some cases can be missed due to reader dependency, resulting in adverse patient outcomes. Hence, it is crucial to implement faster and precise diagnostic strategies to help clinicians diagnose and treat PE patients promptly and mitigate morbidity and mortality. Machine learning (ML) and artificial intelligence (AI) are the newly emerging tools in the medical field, including in radiological imaging, potentially improving diagnostic efficacy. Our review of the studies showed that computer-aided design (CAD) and AI tools displayed similar to superior sensitivity and specificity in identifying PE on CTPA as compared to radiologists. Several tools demonstrated the potential in identifying minor PE on radiological scans showing promising ability to aid clinicians in reducing missed cases substantially. However, it is imperative to design sophisticated tools and conduct large clinical trials to integrate AI use in everyday clinical setting and establish guidelines for its ethical applicability. ML and AI can also potentially help physicians in formulating individualized management strategies to enhance patient outcomes.
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Affiliation(s)
| | | | - Ali Arif
- Medicine, Dow University of Health Sciences, Karachi, PAK
| | | | - David Ospina
- Internal Medicine, Universidad de los Andes, Bogotá, COL
| | | | - Chaithanya Amudha
- Medicine and Surgery, Saveetha Medical College and Hospital, Chennai, IND
| | - Kumareson K Raman
- Cardiology, Nottingham University Hospitals National Health Service (NHS) Trust, Nottingham, GBR
| | - Humza F Siddiqui
- Internal Medicine, Jinnah Postgraduate Medical Centre, Karachi, PAK
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16
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Chrysafi P, Lam B, Carton S, Patell R. From Code to Clots: Applying Machine Learning to Clinical Aspects of Venous Thromboembolism Prevention, Diagnosis, and Management. Hamostaseologie 2024; 44:429-445. [PMID: 39657652 DOI: 10.1055/a-2415-8408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2024] Open
Abstract
The high incidence of venous thromboembolism (VTE) globally and the morbidity and mortality burden associated with the disease make it a pressing issue. Machine learning (ML) can improve VTE prevention, detection, and treatment. The ability of this novel technology to process large amounts of high-dimensional data can help identify new risk factors and better risk stratify patients for thromboprophylaxis. Applications of ML for VTE include systems that interpret medical imaging, assess the severity of the VTE, tailor treatment according to individual patient needs, and identify VTE cases to facilitate surveillance. Generative artificial intelligence may be leveraged to design new molecules such as new anticoagulants, generate synthetic data to expand datasets, and reduce clinical burden by assisting in generating clinical notes. Potential challenges in the applications of these novel technologies include the availability of multidimensional large datasets, prospective studies and clinical trials to ensure safety and efficacy, continuous quality assessment to maintain algorithm accuracy, mitigation of unwanted bias, and regulatory and legal guardrails to protect patients and providers. We propose a practical approach for clinicians to integrate ML into research, from choosing appropriate problems to integrating ML into clinical workflows. ML offers much promise and opportunity for clinicians and researchers in VTE to translate this technology into the clinic and directly benefit the patients.
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Affiliation(s)
- Pavlina Chrysafi
- Department of Medicine, Mount Auburn Hospital, Harvard Medical School, Cambridge, Massachusetts, United States
| | - Barbara Lam
- Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States
- Division of Clinical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States
| | - Samuel Carton
- Department of Computer Science, College of Engineering and Physical Sciences, University of New Hampshire, Durham, New Hampshire, United States
| | - Rushad Patell
- Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States
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17
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Laffafchi S, Ebrahimi A, Kafan S. Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data. Health Inf Sci Syst 2024; 12:17. [PMID: 38464464 PMCID: PMC10917730 DOI: 10.1007/s13755-024-00276-9] [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: 12/15/2022] [Accepted: 01/17/2024] [Indexed: 03/12/2024] Open
Abstract
Pulmonary Embolism (PE) is a life-threatening clinical disease with no specific clinical symptoms and Computed Tomography Angiography (CTA) is used for diagnosis. Clinical decision support scoring systems like Wells and rGeneva based on PE risk factors have been developed to estimate the pre-test probability but are underused, leading to continuous overuse of CTA imaging. This diagnostic study aimed to propose a novel approach for efficient management of PE diagnosis using a two-step interconnected machine learning framework directly by analyzing patients' Electronic Health Records data. First, we performed feature importance analysis according to the result of LightGBM superiority for PE prediction, then four state-of-the-art machine learning methods were applied for PE prediction based on the feature importance results, enabling swift and accurate pre-test diagnosis. Throughout the study patients' data from different departments were collected from Sina educational hospital, affiliated with the Tehran University of medical sciences in Iran. Generally, the Ridge classification method obtained the best performance with an F1 score of 0.96. Extensive experimental findings showed the effectiveness and simplicity of this diagnostic process of PE in comparison with the existing scoring systems. The main strength of this approach centered on PE disease management procedures, which would reduce avoidable invasive CTA imaging and be applied as a primary prognosis of PE, hence assisting the healthcare system, clinicians, and patients by reducing costs and promoting treatment quality and patient satisfaction.
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Affiliation(s)
- Soroor Laffafchi
- Department of Business Administration and Entrepreneurship, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Ahmad Ebrahimi
- Department of Industrial and Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Daneshgah Blvd, Simon Bulivar Blvd, Tehran, Iran
| | - Samira Kafan
- Department of Pulmonary Medicine, Sina Hospital, International Relations Office, Medical School, Tehran University of Medical Sciences, PourSina St., Tehran, 1417613151 Iran
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18
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Cotena M, Ayobi A, Zuchowski C, Junn JC, Weinberg BD, Chang PD, Chow DS, Soun JE, Roca-Sogorb M, Chaibi Y, Quenet S. Enhancing Radiologist Efficiency with AI: A Multi-Reader Multi-Case Study on Aortic Dissection Detection and Prioritization. Diagnostics (Basel) 2024; 14:2689. [PMID: 39682597 DOI: 10.3390/diagnostics14232689] [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: 10/14/2024] [Revised: 11/21/2024] [Accepted: 11/26/2024] [Indexed: 12/18/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Acute aortic dissection (AD) is a life-threatening condition in which early detection can significantly improve patient outcomes and survival. This study evaluates the clinical benefits of integrating a deep learning (DL)-based application for the automated detection and prioritization of AD on chest CT angiographies (CTAs) with a focus on the reduction in the scan-to-assessment time (STAT) and interpretation time (IT). MATERIALS AND METHODS This retrospective Multi-Reader Multi-Case (MRMC) study compared AD detection with and without artificial intelligence (AI) assistance. The ground truth was established by two U.S. board-certified radiologists, while three additional expert radiologists served as readers. Each reader assessed the same CTAs in two phases: assessment unaided by AI assistance (pre-AI arm) and, after a 1-month washout period, assessment aided by device outputs (post-AI arm). STAT and IT metrics were compared between the two arms. RESULTS This study included 285 CTAs (95 per reader, per arm) with a mean patient age of 58.5 years ±14.7 (SD), of which 52% were male and 37% had a prevalence of AD. AI assistance significantly reduced the STAT for detecting 33 true positive AD cases from 15.84 min (95% CI: 13.37-18.31 min) without AI to 5.07 min (95% CI: 4.23-5.91 min) with AI, representing a 68% reduction (p < 0.01). The IT also reduced significantly from 21.22 s (95% CI: 19.87-22.58 s) without AI to 14.17 s (95% CI: 13.39-14.95 s) with AI (p < 0.05). CONCLUSIONS The integration of a DL-based algorithm for AD detection on chest CTAs significantly reduces both the STAT and IT. By prioritizing urgent cases, the AI-assisted approach outperforms the standard First-In, First-Out (FIFO) workflow.
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Affiliation(s)
- Martina Cotena
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France
| | - Angela Ayobi
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France
| | - Colin Zuchowski
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road Northeast, Suite BG20, Atlanta, GA 30322, USA
| | - Jacqueline C Junn
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road Northeast, Suite BG20, Atlanta, GA 30322, USA
| | - Brent D Weinberg
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road Northeast, Suite BG20, Atlanta, GA 30322, USA
| | - Peter D Chang
- Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
- Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Irvine, CA 92697, USA
| | - Daniel S Chow
- Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
- Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Irvine, CA 92697, USA
| | - Jennifer E Soun
- Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA
- Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Irvine, CA 92697, USA
| | | | - Yasmina Chaibi
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France
| | - Sarah Quenet
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France
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Mugu VK, Parikh RS, Carr BM, Khandelwal A. CT pulmonary angiography in the inpatients: A 5-year retrospective view at the end of COVID-19 public health emergency. Medicine (Baltimore) 2024; 103:e40351. [PMID: 39533597 PMCID: PMC11557109 DOI: 10.1097/md.0000000000040351] [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: 04/22/2024] [Accepted: 10/15/2024] [Indexed: 11/16/2024] Open
Abstract
To retrospectively evaluate the trends in utilization and results of computed tomography pulmonary angiography (CTPA study) for detection of acute pulmonary embolism (PE) in the hospital inpatients during different phases of COVID-19 public health emergency. We conducted an Institutional Review Board (IRB)-approved retrospective review of CTPA studies for our hospital inpatients in the years from 2019 to 2023, ranging from the prepandemic year (2019) to the year coinciding with the end of public health declarations (2023). Collected characteristics included patient age, patient sex, and result of the study. The utilization of CTPA studies for inpatients was higher in 2020 to 2023, compared to 2019 (3.8% in 2023 compared to 2.4% in 2019, P < .001). The increase in utilization was also statistically significant in each group, when stratified by age and sex (OR = 1.46 for female, OR = 1.71 for male, OR = 1.93 for elderly, and OR = 1.29 for non-elderly inpatients in 2023, compared to 2019). The positivity rate of acute PE in the inpatients was overall higher in 2021, 2022, and 2023, compared to 2019 (for example, 18.5% in 2023 compared to 14.3% in 2019, P = .01). When stratified by age and sex, only the non-elderly patients continued to have a significantly higher rate of acute PE in 2023, compared to 2019 (OR = 1.39). While studies spanning longer time frames and involving multiple institutions are needed to understand and generalize this conclusion, we conclude that the utilization and positivity rates of CTPA studies in inpatients remains high at the end of the COVID-19 pandemic public health emergency.
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Affiliation(s)
| | | | - Brendan M. Carr
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN
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20
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Ammari S, Camez AO, Ayobi A, Quenet S, Zemmouri A, Mniai EM, Chaibi Y, Franciosini A, Clavel L, Bidault F, Muller S, Lassau N, Balleyguier C, Assi T. Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans. Life (Basel) 2024; 14:1347. [PMID: 39598146 PMCID: PMC11595865 DOI: 10.3390/life14111347] [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: 09/15/2024] [Revised: 10/11/2024] [Accepted: 10/14/2024] [Indexed: 11/29/2024] Open
Abstract
INTRODUCTION The incidence of venous thromboembolism is estimated to be around 3% of cancer patients. However, a majority of incidental pulmonary embolism (iPE) can be overlooked by radiologists in asymptomatic patients, performing CT scans for disease surveillance, which may significantly impact the patient's health and management. Routine imaging in oncology is usually reviewed with delayed hours after the acquisition of images. Nevertheless, the advent of AI in radiology could reduce the risk of the diagnostic delay of iPE by an optimal triage immediately at the acquisition console. This study aimed to determine the accuracy rate of an AI algorithm (CINA-iPE) in detecting iPE and the duration until the management of cancer patients in our center, in addition to describing the characteristics of patients with a confirmed pulmonary embolism (PE). MATERIALS AND METHODS This is a retrospective analysis of the role of Avicenna's CE-certified and FDA-cleared CINA-iPE algorithm in oncology patients treated at Gustave Roussy Cancer Campus. The results obtained from the AI algorithm were compared with the attending radiologist's report and were analyzed by both a radiology resident and a senior radiologist. In case of any discordant results, the reason for this discrepancy was further investigated. The duration between the exact time of the CT scan and analysis was assessed, as well as the duration from the result's report and the start of active management. RESULTS Out of 3047 patients, 104 alerts were detected for iPE (prevalence of 1.3%), while 2942 had negative findings. In total, 36 of the 104 patients had confirmed PE, while 68 alerts were false positives. Only one patient reported as negative by the AI tool was deemed to have a PE by the radiologist. The sensitivity and specificity of the AI model were 97.3% and 97.74%, while the PPV and NPV were 34.62% and 99.97%, respectively. Most causes of FP were artifacts (22 cases, 32.3%) and lymph nodes (11 cases, 16.2%). Seven patients experienced delayed diagnosis, requiring them to return to the ER for treatment after being sent home following their scan. The remaining patients received prompt care immediately after their testing, with a mean delay time of 8.13 h. CONCLUSIONS The addition of an AI system for the detection of unsuspected PEs on chest CT scans in routine oncology care demonstrated a promising efficacy in comparison to human performance. Despite a low prevalence, the sensitivity and specificity of the AI tool reached 97.3% and 97.7%, respectively, with detection of all the reported clinical PEs, except one single case. This study describes the potential synergy between AI and radiologists for an optimal diagnosis of iPE in routine clinical cancer care. CLINICAL RELEVANCE STATEMENT In the oncology field, iPEs are common, with an increased risk of morbidity when missed with a delayed diagnosis. With the assistance of a reliable AI tool, the radiologist can focus on the challenging analysis of oncology results while dealing with urgent diagnosis such as PE by sending the patient straight to the ER (Emergency Room) for prompt treatment.
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Affiliation(s)
- Samy Ammari
- Department of Radiology, Gustave Roussy, 94805 Villejuif, France
- Laboratoire d’Imagerie Biomédicale Multimodale Paris-Saclay, Inserm, CNRS, CEA, BIOMAPS, UMR 1281, Université Paris-Saclay, 94800 Villejuif, France
| | | | - Angela Ayobi
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France (Y.C.); (A.F.)
| | - Sarah Quenet
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France (Y.C.); (A.F.)
| | - Amir Zemmouri
- Department of Radiology, Gustave Roussy, 94805 Villejuif, France
| | - El Mehdi Mniai
- Department of Radiology, Gustave Roussy, 94805 Villejuif, France
| | - Yasmina Chaibi
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France (Y.C.); (A.F.)
| | - Angelo Franciosini
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France (Y.C.); (A.F.)
| | - Louis Clavel
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France (Y.C.); (A.F.)
| | - François Bidault
- Department of Radiology, Gustave Roussy, 94805 Villejuif, France
- Laboratoire d’Imagerie Biomédicale Multimodale Paris-Saclay, Inserm, CNRS, CEA, BIOMAPS, UMR 1281, Université Paris-Saclay, 94800 Villejuif, France
| | - Serge Muller
- Department of Radiology, Gustave Roussy, 94805 Villejuif, France
| | - Nathalie Lassau
- Department of Radiology, Gustave Roussy, 94805 Villejuif, France
- Laboratoire d’Imagerie Biomédicale Multimodale Paris-Saclay, Inserm, CNRS, CEA, BIOMAPS, UMR 1281, Université Paris-Saclay, 94800 Villejuif, France
| | - Corinne Balleyguier
- Department of Radiology, Gustave Roussy, 94805 Villejuif, France
- Laboratoire d’Imagerie Biomédicale Multimodale Paris-Saclay, Inserm, CNRS, CEA, BIOMAPS, UMR 1281, Université Paris-Saclay, 94800 Villejuif, France
| | - Tarek Assi
- Division of International Patients Care, Gustave Roussy, 94805 Villejuif, France
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21
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Klemenz AC, Manzke M, Meinel FG. [Artificial intelligence in cardiovascular radiology : Image acquisition, image reconstruction and workflow optimization]. RADIOLOGIE (HEIDELBERG, GERMANY) 2024; 64:766-772. [PMID: 38913176 DOI: 10.1007/s00117-024-01335-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/05/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to fundamentally change radiology workflow. OBJECTIVES This review article provides an overview of AI applications in cardiovascular radiology with a focus on image acquisition, image reconstruction, and workflow optimization. MATERIALS AND METHODS First, established applications of AI are presented for cardiovascular computed tomography (CT) and magnetic resonance imaging (MRI). Building on this, we describe the range of applications that are currently being developed and evaluated. The practical benefits, opportunities, and potential risks of artificial intelligence in cardiovascular imaging are critically discussed. The presentation is based on the relevant specialist literature and our own clinical and scientific experience. RESULTS AI-based techniques for image reconstruction are already commercially available and enable dose reduction in cardiovascular CT and accelerated image acquisition in cardiac MRI. Postprocessing of cardiovascular CT and MRI examinations can already be considerably simplified using established AI-based segmentation algorithms. In contrast, the practical benefits of many AI applications aimed at the diagnosis of cardiovascular diseases are less evident. Potential risks such as automation bias and considerations regarding cost efficiency should also be taken into account. CONCLUSIONS In a market characterized by great expectations and rapid technical development, it is important to realistically assess the practical benefits of AI applications for your own hospital or practice.
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Affiliation(s)
- Ann-Christin Klemenz
- Universitätsmedizin Rostock, Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Schillingallee 36, 18057, Rostock, Deutschland
| | - Mathias Manzke
- Universitätsmedizin Rostock, Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Schillingallee 36, 18057, Rostock, Deutschland
| | - Felix G Meinel
- Universitätsmedizin Rostock, Institut für Diagnostische und Interventionelle Radiologie, Kinder- und Neuroradiologie, Schillingallee 36, 18057, Rostock, Deutschland.
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22
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Sharkey MJ, Checkley EW, Swift AJ. Applications of artificial intelligence in computed tomography imaging for phenotyping pulmonary hypertension. Curr Opin Pulm Med 2024; 30:464-472. [PMID: 38989815 PMCID: PMC11309337 DOI: 10.1097/mcp.0000000000001103] [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] [Indexed: 07/12/2024]
Abstract
PURPOSE OF REVIEW Pulmonary hypertension is a heterogeneous condition with significant morbidity and mortality. Computer tomography (CT) plays a central role in determining the phenotype of pulmonary hypertension, informing treatment strategies. Many artificial intelligence tools have been developed in this modality for the assessment of pulmonary hypertension. This article reviews the latest CT artificial intelligence applications in pulmonary hypertension and related diseases. RECENT FINDINGS Multistructure segmentation tools have been developed in both pulmonary hypertension and nonpulmonary hypertension cohorts using state-of-the-art UNet architecture. These segmentations correspond well with those of trained radiologists, giving clinically valuable metrics in significantly less time. Artificial intelligence lung parenchymal assessment accurately identifies and quantifies lung disease patterns by integrating multiple radiomic techniques such as texture analysis and classification. This gives valuable information on disease burden and prognosis. There are many accurate artificial intelligence tools to detect acute pulmonary embolism. Detection of chronic pulmonary embolism proves more challenging with further research required. SUMMARY There are numerous artificial intelligence tools being developed to identify and quantify many clinically relevant parameters in both pulmonary hypertension and related disease cohorts. These potentially provide accurate and efficient clinical information, impacting clinical decision-making.
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Affiliation(s)
- Michael J. Sharkey
- Department of Clinical Medicine, University of Sheffield
- 3D Imaging Lab, Sheffield Teaching Hospitals NHS Foundation Trust
| | | | - Andrew J. Swift
- Department of Clinical Medicine, University of Sheffield
- Insigneo Institute for in Silico Medicine, University of Sheffield
- National Institute for Health and Care Research, Sheffield Biomedical Research Centre, Sheffield, UK
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23
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Ayobi A, Chang PD, Chow DS, Weinberg BD, Tassy M, Franciosini A, Scudeler M, Quenet S, Avare C, Chaibi Y. Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection. Clin Imaging 2024; 113:110245. [PMID: 39094243 DOI: 10.1016/j.clinimag.2024.110245] [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: 05/14/2024] [Revised: 07/25/2024] [Accepted: 07/27/2024] [Indexed: 08/04/2024]
Abstract
PURPOSE Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and clinical utility of an artificial intelligence (AI)-based application designed to assist clinicians in the detection of PE on CT pulmonary angiography (CTPA). PATIENTS AND METHODS CTPAs from 230 US cities acquired on 57 scanner models from 6 different vendors were retrospectively collected. Three US board certified expert radiologists defined the ground truth by majority agreement. The same cases were analyzed by CINA-PE, an AI-driven algorithm capable of detecting and highlighting suspected PE locations. The algorithm's performance at a per-case and per-finding level was evaluated. Furthermore, cases with PE not mentioned in the clinical report but correctly detected by the algorithm were analyzed. RESULTS A total of 1204 CTPAs (mean age 62.1 years ± 16.6[SD], 44.4 % female, 14.9 % positive) were included in the study. Per-case sensitivity and specificity were 93.9 % (95%CI: 89.3 %-96.9 %) and 94.8 % (95%CI: 93.3 %-96.1 %), respectively. Per-finding positive predictive value was 89.5 % (95%CI: 86.7 %-91.9 %). Among the 196 positive cases, 29 (15.6 %) were not mentioned in the clinical report. The algorithm detected 22/29 (76 %) of these cases, leading to a reduction in the miss rate from 15.6 % to 3.8 % (7/186). CONCLUSIONS The AI-based application may improve diagnostic accuracy in detecting PE and enhance patient outcomes through timely intervention. Integrating AI tools in clinical workflows can reduce missed or delayed diagnoses, and positively impact healthcare delivery and patient care.
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Affiliation(s)
- Angela Ayobi
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France
| | - Peter D Chang
- Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA; Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Irvine, CA 92697, USA
| | - Daniel S Chow
- Department of Radiological Sciences, University of California Irvine, Irvine, CA 92697, USA; Center for Artificial Intelligence in Diagnostic Medicine, University of California Irvine, Irvine, CA 92697, USA
| | - Brent D Weinberg
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA
| | - Maxime Tassy
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France
| | | | | | - Sarah Quenet
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France
| | | | - Yasmina Chaibi
- Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France
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24
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Hemalakshmi GR, Murugappan M, Sikkandar MY, Santhi D, Prakash NB, Mohanarathinam A. PE-Ynet: a novel attention-based multi-task model for pulmonary embolism detection using CT pulmonary angiography (CTPA) scan images. Phys Eng Sci Med 2024; 47:863-880. [PMID: 38546819 DOI: 10.1007/s13246-024-01410-3] [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: 07/11/2023] [Accepted: 02/19/2024] [Indexed: 09/18/2024]
Abstract
Pulmonary Embolism (PE) has diverse manifestations with different etiologies such as venous thromboembolism, septic embolism, and paradoxical embolism. In this study, a novel attention-based multi-task model is proposed for PE segmentation and detection from Computed Tomography Pulmonary Angiography (CTPA) images. A Y-Net architecture is used to implement this model, which facilitates segmentation and classification jointly, improving performance and efficiency. It is leveraged with Multi Head Attention (MHA), which allows the model to focus on important regions of the image while suppressing irrelevant information, improving the accuracy of the segmentation and detection tasks. The proposed PE-YNet model is tested with two public datasets, achieving a maximum mean detection and segmentation accuracy of 99.89% and 99.83%, respectively, on the CAD-PE challenge dataset. Similarly, it also achieves a detection accuracy of 99.75% and a segmentation accuracy of 99.81% on the FUMPE dataset. Additionally, sensitivity analysis also shows a high sensitivity of 0.9885 for the localization error ɛ = 0 for the CAD-PE dataset, demonstrating the model's robustness against false predictions compared to state-of-the-art models. Further, this model also exhibits lower inference time, size, and memory usage compared to representative models. An automated PE-YNet tool can assist physicians with PE diagnosis, treatment, and prognosis monitoring in the clinical management of CoVID-19.
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Affiliation(s)
- G R Hemalakshmi
- School of Computing Science and Engineering, Vellore Institute of Technology, Bhopal, Madhya Pradesh, India
| | - M Murugappan
- Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, 13133, Doha, Kuwait.
- Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai, Tamil Nadu, India.
- Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia.
| | - Mohamed Yacin Sikkandar
- Biomedical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majma'ah, Saudi Arabia
| | - D Santhi
- Department of Biomedical Engineering, Mepco Schlenk Engineering College, Sivakasi, India
| | - N B Prakash
- Department of Electrical and Electronics Engineering, National Engineering College, Kovilpatti, India
| | - A Mohanarathinam
- Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, 641021, India
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25
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da Silva LO, da Silva MCB, Ribeiro GAS, de Camargo TFO, dos Santos PV, Mendes GDS, de Paiva JPQ, Soares ADS, Reis MRDC, Loureiro RM, Calixto WP. Artificial intelligence-based pulmonary embolism classification: Development and validation using real-world data. PLoS One 2024; 19:e0305839. [PMID: 39167612 PMCID: PMC11338462 DOI: 10.1371/journal.pone.0305839] [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: 03/12/2024] [Accepted: 06/05/2024] [Indexed: 08/23/2024] Open
Abstract
This paper presents an artificial intelligence-based classification model for the detection of pulmonary embolism in computed tomography angiography. The proposed model, developed from public data and validated on a large dataset from a tertiary hospital, uses a two-dimensional approach that integrates temporal series to classify each slice of the examination and make predictions at both slice and examination levels. The training process consists of two stages: first using a convolutional neural network InceptionResNet V2 and then a recurrent neural network long short-term memory model. This approach achieved an accuracy of 93% at the slice level and 77% at the examination level. External validation using a hospital dataset resulted in a precision of 86% for positive pulmonary embolism cases and 69% for negative pulmonary embolism cases. Notably, the model excels in excluding pulmonary embolism, achieving a precision of 73% and a recall of 82%, emphasizing its clinical value in reducing unnecessary interventions. In addition, the diverse demographic distribution in the validation dataset strengthens the model's generalizability. Overall, this model offers promising potential for accurate detection and exclusion of pulmonary embolism, potentially streamlining diagnosis and improving patient outcomes.
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Affiliation(s)
- Luan Oliveira da Silva
- Department of Radiology, Hospital Israelita Albert Einstein, Sao Paulo, Brazil
- Institute of Informatics (INF), Federal University of Goias, Goiania, Brazil
| | | | | | - Thiago Fellipe Ortiz de Camargo
- Department of Radiology, Hospital Israelita Albert Einstein, Sao Paulo, Brazil
- Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil
| | - Paulo Victor dos Santos
- Department of Radiology, Hospital Israelita Albert Einstein, Sao Paulo, Brazil
- Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil
| | | | | | | | - Márcio Rodrigues da Cunha Reis
- Department of Radiology, Hospital Israelita Albert Einstein, Sao Paulo, Brazil
- Technology Research and Development Center (GCITE), Federal Institute of Goias, Goias, Brazil
| | | | - Wesley Pacheco Calixto
- Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil
- Technology Research and Development Center (GCITE), Federal Institute of Goias, Goias, Brazil
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26
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Lanza E, Ammirabile A, Francone M. nnU-Net-based deep-learning for pulmonary embolism: detection, clot volume quantification, and severity correlation in the RSPECT dataset. Eur J Radiol 2024; 177:111592. [PMID: 38968751 DOI: 10.1016/j.ejrad.2024.111592] [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: 05/02/2024] [Revised: 06/17/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024]
Abstract
OBJECTIVES CT pulmonary angiography is the gold standard for diagnosing pulmonary embolism, and DL algorithms are being developed to manage the increase in demand. The nnU-Net is a new auto-adaptive DL framework that minimizes manual tuning, making it easier to develop effective algorithms for medical imaging even without specific expertise. This study assesses the performance of a locally developed nnU-Net algorithm on the RSPECT dataset for PE detection, clot volume measurement, and correlation with right ventricle overload. MATERIALS & METHODS User input was limited to segmentation using 3DSlicer. We worked with the RSPECT dataset and trained an algorithm from 205 PE and 340 negatives. The test dataset comprised 6573 exams. Performance was tested against PE characteristics, such as central, non-central, and RV overload. Blood clot volume (BCV) was extracted from each exam. We employed ROC curves and logistic regression for statistical validation. RESULTS Negative studies had a median BCV of 1 μL, which increased to 345 μL in PE-positive cases and 7,378 μL in central PEs. Statistical analysis confirmed a significant BCV correlation with PE presence, central PE, and increased RV/LV ratio (p < 0.0001). The model's AUC for PE detection was 0.865, with an 83 % accuracy at a 55 μL threshold. Central PE detection AUC was 0.937 with 91 % accuracy at 850 μL. The RV overload AUC stood at 0.848 with 79 % accuracy. CONCLUSION The nnU-Net algorithm demonstrated accurate PE detection, particularly for central PE. BCV is an accurate metric for automated severity stratification and case prioritization. CLINICAL RELEVANCE STATEMENT The nnU-Net framework can be utilized to create a dependable DL for detecting PE. It offers a user-friendly approach to those lacking expertise in AI and rapidly extracts the Blood Clot Volume, a metric that can evaluate the PE's severity.
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Affiliation(s)
- Ezio Lanza
- Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini, 4, Pieve Emanuele MI 20072, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy.
| | - Angela Ammirabile
- Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini, 4, Pieve Emanuele MI 20072, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
| | - Marco Francone
- Humanitas University, Department of Biomedical Sciences, Via Rita Levi Montalcini, 4, Pieve Emanuele MI 20072, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
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27
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Motiwala A, Tanwir H, Duarte A, Gilani S, DeAnda A, Zaidan MF, Jneid H. Multidisciplinary Approach to Pulmonary Embolism and the Role of the Pulmonary Embolism Response Team. Curr Cardiol Rep 2024; 26:843-849. [PMID: 38963612 DOI: 10.1007/s11886-024-02084-9] [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] [Accepted: 06/17/2024] [Indexed: 07/05/2024]
Abstract
PURPOSE OF REVIEW Acute pulmonary embolism (PE) is a leading cause of cardiovascular death and morbidity, and presents a major burden to healthcare systems. The field has seen rapid growth with development of innovative clot reduction technologies, as well as ongoing multicenter trials that may completely revolutionize care of PE patients. However, current paucity of robust clinical trials and guidelines often leave individual physicians managing patients with acute PE in a dilemma. RECENT FINDINGS The pulmonary embolism response team (PERT) was developed as a platform to rapidly engage multiple specialists to deliver evidence-based, organized and efficient care and help address some of the gaps in knowledge. Several centers investigating outcomes following implementation of PERT have demonstrated shorter hospital and intensive-care unit stays, lower use of inferior vena cava filters, and in some instances improved mortality. Since the advent of PERT, early findings demonstrate promise with improved outcomes after implementation of PERT. Incorporation of artificial intelligence (AI) into PERT has also shown promise with more streamlined care and reducing response times. Further clinical trials are needed to examine the impact of PERT model on care delivery and clinical outcomes.
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Affiliation(s)
- Afaq Motiwala
- University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA.
| | - Hira Tanwir
- University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA
| | - Alexander Duarte
- University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA
| | - Syed Gilani
- University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA
| | - Abe DeAnda
- University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA
| | | | - Hani Jneid
- University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA
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28
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Kaya K, Gietzen C, Hahnfeldt R, Zoubi M, Emrich T, Halfmann MC, Sieren MM, Elser Y, Krumm P, Brendel JM, Nikolaou K, Haag N, Borggrefe J, Krüchten RV, Müller-Peltzer K, Ehrengut C, Denecke T, Hagendorff A, Goertz L, Gertz RJ, Bunck AC, Maintz D, Persigehl T, Lennartz S, Luetkens JA, Jaiswal A, Iuga AI, Pennig L, Kottlors J. Generative Pre-trained Transformer 4 analysis of cardiovascular magnetic resonance reports in suspected myocarditis: A multicenter study. J Cardiovasc Magn Reson 2024; 26:101068. [PMID: 39079602 PMCID: PMC11414660 DOI: 10.1016/j.jocmr.2024.101068] [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: 04/18/2024] [Revised: 07/04/2024] [Accepted: 07/24/2024] [Indexed: 09/13/2024] Open
Abstract
BACKGROUND Diagnosing myocarditis relies on multimodal data, including cardiovascular magnetic resonance (CMR), clinical symptoms, and blood values. The correct interpretation and integration of CMR findings require radiological expertise and knowledge. We aimed to investigate the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model, for report-based medical decision-making in the context of cardiac MRI for suspected myocarditis. METHODS This retrospective study includes CMR reports from 396 patients with suspected myocarditis and eight centers, respectively. CMR reports and patient data including blood values, age, and further clinical information were provided to GPT-4 and radiologists with 1 (resident 1), 2 (resident 2), and 4 years (resident 3) of experience in CMR and knowledge of the 2018 Lake Louise Criteria. The final impression of the report regarding the radiological assessment of whether myocarditis is present or not was not provided. The performance of Generative pre-trained transformer 4 (GPT-4) and the human readers were compared to a consensus reading (two board-certified radiologists with 8 and 10 years of experience in CMR). Sensitivity, specificity, and accuracy were calculated. RESULTS GPT-4 yielded an accuracy of 83%, sensitivity of 90%, and specificity of 78%, which was comparable to the physician with 1 year of experience (R1: 86%, 90%, 84%, p = 0.14) and lower than that of more experienced physicians (R2: 89%, 86%, 91%, p = 0.007 and R3: 91%, 85%, 96%, p < 0.001). GPT-4 and human readers showed a higher diagnostic performance when results from T1- and T2-mapping sequences were part of the reports, for residents 1 and 3 with statistical significance (p = 0.004 and p = 0.02, respectively). CONCLUSION GPT-4 yielded good accuracy for diagnosing myocarditis based on CMR reports in a large dataset from multiple centers and therefore holds the potential to serve as a diagnostic decision-supporting tool in this capacity, particularly for less experienced physicians. Further studies are required to explore the full potential and elucidate educational aspects of the integration of large language models in medical decision-making.
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Affiliation(s)
- Kenan Kaya
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
| | - Carsten Gietzen
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Robert Hahnfeldt
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Maher Zoubi
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Tilman Emrich
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes-Gutenberg-University, Mainz, Germany; Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA; German Centre for Cardiovascular Research, Partner Site Rhine-Main, Mainz, Germany
| | - Moritz C Halfmann
- Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes-Gutenberg-University, Mainz, Germany
| | - Malte Maria Sieren
- Department of Radiology and Nuclear Medicine, UKSH, Campus Lübeck, Lübeck, Germany; Institute of Interventional Radiology, UKSH, Campus Lübeck, Lübeck, Germany
| | - Yannic Elser
- Department of Radiology and Nuclear Medicine, UKSH, Campus Lübeck, Lübeck, Germany
| | - Patrick Krumm
- Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany
| | - Jan M Brendel
- Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany
| | - Konstantin Nikolaou
- Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany
| | - Nina Haag
- Institute for Radiology, Neuroradiology and Nuclear Medicine Johannes Wesling University Hospital/Mühlenkreiskliniken, Bochum/Minden, Germany
| | - Jan Borggrefe
- Institute for Radiology, Neuroradiology and Nuclear Medicine Johannes Wesling University Hospital/Mühlenkreiskliniken, Bochum/Minden, Germany
| | - Ricarda von Krüchten
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Katharina Müller-Peltzer
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Constantin Ehrengut
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | - Timm Denecke
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany
| | | | - Lukas Goertz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Roman J Gertz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Alexander Christian Bunck
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - David Maintz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Simon Lennartz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Julian A Luetkens
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Astha Jaiswal
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Andra Iza Iuga
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lenhard Pennig
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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de Jong CMM, Kroft LJM, van Mens TE, Huisman MV, Stöger JL, Klok FA. Modern imaging of acute pulmonary embolism. Thromb Res 2024; 238:105-116. [PMID: 38703584 DOI: 10.1016/j.thromres.2024.04.016] [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: 11/24/2023] [Revised: 03/16/2024] [Accepted: 04/15/2024] [Indexed: 05/06/2024]
Abstract
The first-choice imaging test for visualization of thromboemboli in the pulmonary vasculature in patients with suspected acute pulmonary embolism (PE) is multidetector computed tomography pulmonary angiography (CTPA) - a readily available and widely used imaging technique. Through technological advancements over the past years, alternative imaging techniques for the diagnosis of PE have become available, whilst others are still under investigation. In particular, the evolution of artificial intelligence (AI) is expected to enable further innovation in diagnostic management of PE. In this narrative review, current CTPA techniques and the emerging technology photon-counting CT (PCCT), as well as other modern imaging techniques of acute PE are discussed, including CTPA with iodine maps based on subtraction or dual-energy acquisition, single-photon emission CT (SPECT), magnetic resonance angiography (MRA), and magnetic resonance direct thrombus imaging (MRDTI). Furthermore, potential applications of AI are discussed.
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Affiliation(s)
- C M M de Jong
- Department of Medicine - Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, the Netherlands
| | - L J M Kroft
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - T E van Mens
- Department of Medicine - Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, the Netherlands
| | - M V Huisman
- Department of Medicine - Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, the Netherlands
| | - J L Stöger
- Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - F A Klok
- Department of Medicine - Thrombosis and Hemostasis, Leiden University Medical Center, Leiden, the Netherlands.
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Mugu VK, Carr BM, Olson MC, Khandelwal A. CT pulmonary angiography in the emergency department: utilization and positivity rates during various phases of the COVID-19 pandemic. Emerg Radiol 2024; 31:293-301. [PMID: 38519743 DOI: 10.1007/s10140-024-02218-0] [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: 01/29/2024] [Accepted: 03/11/2024] [Indexed: 03/25/2024]
Abstract
PURPOSE To evaluate the trends in utilization and results of computed tomography pulmonary angiography (CTPA study) for detection of acute pulmonary embolism (PE) in the Emergency Department (ED) during different phases of COVID-19 public health emergency. METHODS We conducted a retrospective review of CTPA studies ordered through our ED in the months of March through May during five consecutive years from 2019 to 2023, designated as pre-pandemic, early, ongoing, recovery, and post-pandemic periods respectively. Collected characteristics included patient age, patient sex, and result of the study. RESULTS The utilization of CTPA studies for ED patients increased during the early, ongoing, and recovery periods. CTPA study utilization in the post-pandemic period was not significantly different from the pre-pandemic period (p = 0.08). No significant difference in CTPA study utilization was noted in the other periods when stratified by age group or sex, compared to the pre-pandemic period. The positivity rate of acute PE in ED patients was not significantly different in other periods compared to the pre-pandemic period. CONCLUSION At our institution, the utilization and positivity rates of CTPA studies for the ED patients were not significantly different in the post-pandemic period compared to the pre-pandemic period. While studies spanning a larger timeframe and involving multiple institutions are needed to test the applicability of this observation to a wider patient population beyond our defined post-pandemic period, we conclude that our study provides some confidence to the ordering provider and the radiologist in embracing the end of COVID-19 public health emergency by the WHO and the United States HHS with respect to CTPA studies.
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Affiliation(s)
- Vamshi K Mugu
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
| | - Brendan M Carr
- Department of Emergency Medicine, Mayo Clinic, Rochester, MN, USA
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Condrea F, Rapaka S, Itu L, Sharma P, Sperl J, Ali AM, Leordeanu M. Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms. Comput Biol Med 2024; 174:108464. [PMID: 38613894 DOI: 10.1016/j.compbiomed.2024.108464] [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: 06/22/2023] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 04/15/2024]
Abstract
Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, through computed tomographic pulmonary angiography (CTPA), represents the gold standard for PE diagnosis, it is still susceptible to misdiagnosis or significant diagnosis delays, which may be fatal for critical cases. Despite the recently demonstrated power of deep learning to bring a significant boost in performance in a wide range of medical imaging tasks, there are still very few published researches on automatic pulmonary embolism detection. Herein we introduce a deep learning based approach, which efficiently combines computer vision and deep neural networks for pulmonary embolism detection in CTPA. Our method brings novel contributions along three orthogonal axes: (1) automatic detection of anatomical structures; (2) anatomical aware pretraining, and (3) a dual-hop deep neural net for PE detection. We obtain state-of-the-art results on the publicly available multicenter large-scale RSNA dataset.
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Affiliation(s)
- Florin Condrea
- Institute of Mathematics of the Romanian Academy "Simion Stoilow, Bucharest, Romania; Advanta, Siemens, 15 Noiembrie Bvd, Brasov, 500097, Romania.
| | | | - Lucian Itu
- Advanta, Siemens, 15 Noiembrie Bvd, Brasov, 500097, Romania
| | | | | | - A Mohamed Ali
- Siemens Healthcare Private Limited, Mumbai, 400079, India
| | - Marius Leordeanu
- Institute of Mathematics of the Romanian Academy "Simion Stoilow, Bucharest, Romania; Advanta, Siemens, 15 Noiembrie Bvd, Brasov, 500097, Romania; Polytechnic University of Bucharest, Bucharest, Romania
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Li H, Hayward J, Aguilar LS, Franc JM. Desired clinical applications of artificial intelligence in emergency medicine: A Delphi study. Am J Emerg Med 2024; 79:217-220. [PMID: 38458952 DOI: 10.1016/j.ajem.2024.02.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 02/01/2024] [Accepted: 02/08/2024] [Indexed: 03/10/2024] Open
Affiliation(s)
- Henry Li
- University of Alberta, Faculty of Medicine and Dentistry, Department of Emergency Medicine, 750 University Terrace Building, 8303-112 Street NW, Edmonton T6G 2T4, Canada.
| | - Jake Hayward
- University of Alberta, Faculty of Medicine and Dentistry, Department of Emergency Medicine, 750 University Terrace Building, 8303-112 Street NW, Edmonton T6G 2T4, Canada
| | - Leandro Solis Aguilar
- University of Alberta, Faculty of Medicine and Dentistry, Department of Biochemistry, 474 Medical Sciences Building, Edmonton T6G 2H7, Canada
| | - Jeffrey Michael Franc
- University of Alberta, Faculty of Medicine and Dentistry, Department of Emergency Medicine, 750 University Terrace Building, 8303-112 Street NW, Edmonton T6G 2T4, Canada; Università del Piemonte Orientale, Center for Research and Training in Disaster Medicine, Humanitarian Aid, and Global Health, Via Lanino 1, Novara 28100, Italy
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Naser JA, Lee E, Pislaru SV, Tsaban G, Malins JG, Jackson JI, Anisuzzaman DM, Rostami B, Lopez-Jimenez F, Friedman PA, Kane GC, Pellikka PA, Attia ZI. Artificial intelligence-based classification of echocardiographic views. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:260-269. [PMID: 38774376 PMCID: PMC11104471 DOI: 10.1093/ehjdh/ztae015] [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: 05/20/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 05/24/2024]
Abstract
Aims Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results We trained two- and three-dimensional convolutional neural networks (CNNs) using transthoracic echocardiographic (TTE) studies obtained from 909 patients to classify nine view categories (10 269 videos). Transthoracic echocardiographic studies from 229 patients were used in internal validation (2582 videos). Convolutional neural networks were tested on 100 patients with comprehensive TTE studies (where the two examples chosen by CNNs as most likely to represent a view were evaluated) and 408 patients with five view categories obtained via point-of-care ultrasound (POCUS). The overall accuracy of the two-dimensional CNN was 96.8%, and the averaged area under the curve (AUC) was 0.997 on the comprehensive TTE testing set; these numbers were 98.4% and 0.998, respectively, on the POCUS set. For the three-dimensional CNN, the accuracy and AUC were 96.3% and 0.998 for full TTE studies and 95.0% and 0.996 on POCUS videos, respectively. The positive predictive value, which defined correctly identified predicted views, was higher with two-dimensional rather than three-dimensional networks, exceeding 93% in apical, short-axis aortic valve, and parasternal long-axis left ventricle views. Conclusion An automated view classifier utilizing CNNs was able to classify cardiac views obtained using TTE and POCUS with high accuracy. The view classifier will facilitate the application of deep learning to echocardiography.
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Affiliation(s)
- Jwan A Naser
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Eunjung Lee
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Sorin V Pislaru
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Gal Tsaban
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Jeffrey G Malins
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - John I Jackson
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - D M Anisuzzaman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Behrouz Rostami
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Francisco Lopez-Jimenez
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Garvan C Kane
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Patricia A Pellikka
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
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Langius-Wiffen E, Slotman DJ, Groeneveld J, Ac van Osch J, Nijholt IM, de Boer E, Nijboer-Oosterveld J, Veldhuis WB, de Jong PA, Boomsma MF. External validation of the RSNA 2020 pulmonary embolism detection challenge winning deep learning algorithm. Eur J Radiol 2024; 173:111361. [PMID: 38401407 DOI: 10.1016/j.ejrad.2024.111361] [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: 10/19/2023] [Revised: 01/17/2024] [Accepted: 02/08/2024] [Indexed: 02/26/2024]
Abstract
PURPOSE To evaluate the diagnostic performance and generalizability of the winning DL algorithm of the RSNA 2020 PE detection challenge to a local population using CTPA data from two hospitals. MATERIALS AND METHODS Consecutive CTPA images from patients referred for suspected PE were retrospectively analysed. The winning RSNA 2020 DL algorithm was retrained on the RSNA-STR Pulmonary Embolism CT (RSPECT) dataset. The algorithm was tested in hospital A on multidetector CT (MDCT) images of 238 patients and in hospital B on spectral detector CT (SDCT) and virtual monochromatic images (VMI) of 114 patients. The output of the DL algorithm was compared with a reference standard, which included a consensus reading by at least two experienced cardiothoracic radiologists for both hospitals. Areas under the receiver operating characteristic curve (AUCs) were calculated. Sensitivity and specificity were determined using the maximum Youden index. RESULTS According to the reference standard, PE was present in 73 patients (30.7%) in hospital A and 33 patients (29.0%) in hospital B. For the DL algorithm the AUC was 0.96 (95% CI 0.92-0.98) in hospital A, 0.89 (95% CI 0.81-0.94) for conventional reconstruction in hospital B and 0.87 (95% CI 0.80-0.93) for VMI. CONCLUSION The RSNA 2020 pulmonary embolism detection on CTPA challenge winning DL algorithm, retrained on the RSPECT dataset, showed high diagnostic accuracy on MDCT images. A somewhat lower performance was observed on SDCT images, which suggest additional training on novel CT technology may improve generalizability of this DL algorithm.
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Affiliation(s)
| | - Derk J Slotman
- Department of Radiology, Isala Hospital, Zwolle, the Netherlands; Department of Radiology, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Jorik Groeneveld
- Department of Radiology, Isala Hospital, Zwolle, the Netherlands
| | | | - Ingrid M Nijholt
- Department of Radiology, Isala Hospital, Zwolle, the Netherlands; Department of Radiology, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Erwin de Boer
- Department of Radiology, Isala Hospital, Zwolle, the Netherlands
| | | | - Wouter B Veldhuis
- Department of Radiology, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital, Zwolle, the Netherlands; Department of Radiology, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
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Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel) 2024; 11:337. [PMID: 38671759 PMCID: PMC11047988 DOI: 10.3390/bioengineering11040337] [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: 02/28/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
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Affiliation(s)
| | - Mohamad Forouzanfar
- Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada
- Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada
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Lindner C, Riquelme R, San Martín R, Quezada F, Valenzuela J, Maureira JP, Einersen M. Improving the radiological diagnosis of hepatic artery thrombosis after liver transplantation: Current approaches and future challenges. World J Transplant 2024; 14:88938. [PMID: 38576750 PMCID: PMC10989478 DOI: 10.5500/wjt.v14.i1.88938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/03/2023] [Accepted: 12/29/2023] [Indexed: 03/15/2024] Open
Abstract
Hepatic artery thrombosis (HAT) is a devastating vascular complication following liver transplantation, requiring prompt diagnosis and rapid revascularization treatment to prevent graft loss. At present, imaging modalities such as ultrasound, computed tomography, and magnetic resonance play crucial roles in diagnosing HAT. Although imaging techniques have improved sensitivity and specificity for HAT diagnosis, they have limitations that hinder the timely diagnosis of this complication. In this sense, the emergence of artificial intelligence (AI) presents a transformative opportunity to address these diagnostic limitations. The develo pment of machine learning algorithms and deep neural networks has demon strated the potential to enhance the precision diagnosis of liver transplant com plications, enabling quicker and more accurate detection of HAT. This article examines the current landscape of imaging diagnostic techniques for HAT and explores the emerging role of AI in addressing future challenges in the diagnosis of HAT after liver transplant.
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Affiliation(s)
- Cristian Lindner
- Department of Radiology, Faculty of Medicine, University of Concepción, Concepción 4030000, Chile
- Department of Radiology, Hospital Clínico Regional Guillermo Grant Benavente, Concepción 4030000, Chile
| | - Raúl Riquelme
- Department of Radiology, Faculty of Medicine, University of Concepción, Concepción 4030000, Chile
- Department of Radiology, Hospital Clínico Regional Guillermo Grant Benavente, Concepción 4030000, Chile
| | - Rodrigo San Martín
- Department of Radiology, Faculty of Medicine, University of Concepción, Concepción 4030000, Chile
- Department of Radiology, Hospital Clínico Regional Guillermo Grant Benavente, Concepción 4030000, Chile
| | - Frank Quezada
- Department of Radiology, Faculty of Medicine, University of Concepción, Concepción 4030000, Chile
- Department of Radiology, Hospital Clínico Regional Guillermo Grant Benavente, Concepción 4030000, Chile
| | - Jorge Valenzuela
- Department of Radiology, Faculty of Medicine, University of Concepción, Concepción 4030000, Chile
- Department of Radiology, Hospital Clínico Regional Guillermo Grant Benavente, Concepción 4030000, Chile
| | - Juan P Maureira
- Department of Statistics, Catholic University of Maule, Talca 3460000, Chile
| | - Martín Einersen
- Department of Radiology, Faculty of Medicine, University of Concepción, Concepción 4030000, Chile
- Neurovascular Unit, Department of Radiology, Hospital Clínico Regional Guillermo Grant Benavente, Concepción 4030000, Chile
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Faghani S, Patel S, Rhodes NG, Powell GM, Baffour FI, Moassefi M, Glazebrook KN, Erickson BJ, Tiegs-Heiden CA. Deep-learning for automated detection of MSU deposits on DECT: evaluating impact on efficiency and reader confidence. FRONTIERS IN RADIOLOGY 2024; 4:1330399. [PMID: 38440382 PMCID: PMC10909828 DOI: 10.3389/fradi.2024.1330399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 01/31/2024] [Indexed: 03/06/2024]
Abstract
Introduction Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Manually identifying these foci (most commonly labeled green) is tedious, and an automated detection system could streamline the process. This study aims to evaluate the impact of a deep-learning (DL) algorithm developed for detecting green pixelations on DECT on reader time, accuracy, and confidence. Methods We collected a sample of positive and negative DECTs, reviewed twice-once with and once without the DL tool-with a 2-week washout period. An attending musculoskeletal radiologist and a fellow separately reviewed the cases, simulating clinical workflow. Metrics such as time taken, confidence in diagnosis, and the tool's helpfulness were recorded and statistically analyzed. Results We included thirty DECTs from different patients. The DL tool significantly reduced the reading time for the trainee radiologist (p = 0.02), but not for the attending radiologist (p = 0.15). Diagnostic confidence remained unchanged for both (p = 0.45). However, the DL model identified tiny MSU deposits that led to a change in diagnosis in two cases for the in-training radiologist and one case for the attending radiologist. In 3/3 of these cases, the diagnosis was correct when using DL. Conclusions The implementation of the developed DL model slightly reduced reading time for our less experienced reader and led to improved diagnostic accuracy. There was no statistically significant difference in diagnostic confidence when studies were interpreted without and with the DL model.
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Affiliation(s)
- Shahriar Faghani
- Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Soham Patel
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | | | - Garret M. Powell
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | | | - Mana Moassefi
- Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | | | - Bradley J. Erickson
- Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN, United States
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Vallée A, Quint R, Laure Brun A, Mellot F, Grenier PA. A deep learning-based algorithm improves radiology residents' diagnoses of acute pulmonary embolism on CT pulmonary angiograms. Eur J Radiol 2024; 171:111324. [PMID: 38241853 DOI: 10.1016/j.ejrad.2024.111324] [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: 10/03/2023] [Revised: 12/08/2023] [Accepted: 01/15/2024] [Indexed: 01/21/2024]
Abstract
PURPOSE To compare radiology residents' diagnostic performances to detect pulmonary emboli (PEs) on CT pulmonary angiographies (CTPAs) with deep-learning (DL)-based algorithm support and without. METHODS Fully anonymized CTPAs (n = 207) of patients suspected of having acute PE served as input for PE detection using a previously trained and validated DL-based algorithm. Three residents in their first three years of training, blinded to the index report and clinical history, read the CTPAs first without, and 2 months later with the help of artificial intelligence (AI) output, to diagnose PE as present, absent or indeterminate. We evaluated concordances and discordances with the consensus-reading results of two experts in chest imaging. RESULTS Because the AI algorithm failed to analyze 11 CTPAs, 196 CTPAs were analyzed; 31 (15.8 %) were PE-positive. Good-classification performance was higher for residents with AI-algorithm support than without (AUROCs: 0.958 [95 % CI: 0.921-0.979] vs. 0.894 [95 % CI: 0.850-0.931], p < 0.001, respectively). The main finding was the increased sensitivity of residents' diagnoses using the AI algorithm (92.5 % vs. 81.7 %, respectively). Concordance between residents (kappa: 0.77 [95 % CI: 0.76-0.78]; p < 0.001) improved with AI-algorithm use (kappa: 0.88 [95 % CI: 0.87-0.89]; p < 0.001). CONCLUSION The AI algorithm we used improved between-resident agreements to interpret CTPAs for suspected PE and, hence, their diagnostic performances.
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Affiliation(s)
- Alexandre Vallée
- Department of Epidemiology and Public Health, Hôpital Foch. 40 rue Worth 92150 Suresnes, France.
| | - Raphaelle Quint
- Department of Medical Imaging, Hôpital Foch. 40 rue Worth 92150 Suresnes, France.
| | - Anne Laure Brun
- Department of Medical Imaging, Hôpital Foch. 40 rue Worth 92150 Suresnes, France.
| | - François Mellot
- Department of Medical Imaging, Hôpital Foch. 40 rue Worth 92150 Suresnes, France.
| | - Philippe A Grenier
- Department of Clinical Research and Innovation, Hôpital Foch. 40 rue Worth 92150 Suresnes, France.
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Grenier PA, Brun AL, Mellot F. [The contribution of artificial intelligence (AI) subsequent to the processing of thoracic imaging]. Rev Mal Respir 2024; 41:110-126. [PMID: 38129269 DOI: 10.1016/j.rmr.2023.12.001] [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: 10/24/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
The contribution of artificial intelligence (AI) to medical imaging is currently the object of widespread experimentation. The development of deep learning (DL) methods, particularly convolution neural networks (CNNs), has led to performance gains often superior to those achieved by conventional methods such as machine learning. Radiomics is an approach aimed at extracting quantitative data not accessible to the human eye from images expressing a disease. The data subsequently feed machine learning models and produce diagnostic or prognostic probabilities. As for the multiple applications of AI methods in thoracic imaging, they are undergoing evaluation. Chest radiography is a practically ideal field for the development of DL algorithms able to automatically interpret X-rays. Current algorithms can detect up to 14 different abnormalities present either in isolation or in combination. Chest CT is another area offering numerous AI applications. Various algorithms have been specifically formed and validated for the detection and characterization of pulmonary nodules and pulmonary embolism, as well as segmentation and quantitative analysis of the extent of diffuse lung diseases (emphysema, infectious pneumonias, interstitial lung disease). In addition, the analysis of medical images can be associated with clinical, biological, and functional data (multi-omics analysis), the objective being to construct predictive approaches regarding disease prognosis and response to treatment.
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Affiliation(s)
- P A Grenier
- Délégation à la recherche clinique et l'innovation, hôpital Foch, Suresnes, France.
| | - A L Brun
- Service de radiologie, hôpital Foch, Suresnes, France
| | - F Mellot
- Service de radiologie, hôpital Foch, Suresnes, France
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Sun Z, Silberstein J, Vaccarezza M. Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment. J Cardiovasc Dev Dis 2024; 11:22. [PMID: 38248892 PMCID: PMC10816599 DOI: 10.3390/jcdd11010022] [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: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
Cardiovascular CT is being widely used in the diagnosis of cardiovascular disease due to the rapid technological advancements in CT scanning techniques. These advancements include the development of multi-slice CT, from early generation to the latest models, which has the capability of acquiring images with high spatial and temporal resolution. The recent emergence of photon-counting CT has further enhanced CT performance in clinical applications, providing improved spatial and contrast resolution. CT-derived fractional flow reserve is superior to standard CT-based anatomical assessment for the detection of lesion-specific myocardial ischemia. CT-derived 3D-printed patient-specific models are also superior to standard CT, offering advantages in terms of educational value, surgical planning, and the simulation of cardiovascular disease treatment, as well as enhancing doctor-patient communication. Three-dimensional visualization tools including virtual reality, augmented reality, and mixed reality are further advancing the clinical value of cardiovascular CT in cardiovascular disease. With the widespread use of artificial intelligence, machine learning, and deep learning in cardiovascular disease, the diagnostic performance of cardiovascular CT has significantly improved, with promising results being presented in terms of both disease diagnosis and prediction. This review article provides an overview of the applications of cardiovascular CT, covering its performance from the perspective of its diagnostic value based on traditional lumen assessment to the identification of vulnerable lesions for the prediction of disease outcomes with the use of these advanced technologies. The limitations and future prospects of these technologies are also discussed.
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Affiliation(s)
- Zhonghua Sun
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
- Curtin Health Innovation Research Institute (CHIRI), Curtin University, Perth, WA 6102, Australia
| | - Jenna Silberstein
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
| | - Mauro Vaccarezza
- Curtin Medical School, Curtin University, Perth, WA 6102, Australia; (J.S.); (M.V.)
- Curtin Health Innovation Research Institute (CHIRI), Curtin University, Perth, WA 6102, Australia
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Langius-Wiffen E, Nijholt IM, van Dijk RA, de Boer E, Nijboer-Oosterveld J, Veldhuis WB, de Jong PA, Boomsma MF. An artificial intelligence algorithm for pulmonary embolism detection on polychromatic computed tomography: performance on virtual monochromatic images. Eur Radiol 2024; 34:384-390. [PMID: 37542651 DOI: 10.1007/s00330-023-10048-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 08/07/2023]
Abstract
OBJECTIVES Virtual monochromatic images (VMI) are increasingly used in clinical practice as they improve contrast-to-noise ratio. However, due to their different appearances, the performance of artificial intelligence (AI) trained on conventional CT images may worsen. The goal of this study was to assess the performance of an established AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPI) to detect pulmonary embolism (PE) on VMI. METHODS Paired 60 kiloelectron volt (keV) VMI and CPI of 114 consecutive patients suspected of PE, obtained with a detector-based spectral CT scanner, were retrospectively analyzed by an established AI algorithm. The CT pulmonary angiography (CTPA) were classified as positive or negative for PE on a per-patient level. The reference standard was established using a comprehensive method that combined the evaluation of the attending radiologist and three experienced cardiothoracic radiologists aided by two different detection tools. Sensitivity, specificity, positive and negative predictive values and likelihood ratios of the algorithm on VMI and CPI were compared. RESULTS The prevalence of PE according to the reference standard was 35.1% (40 patients). None of the diagnostic accuracy measures of the algorithm showed a significant difference between CPI and VMI. Sensitivity was 77.5% (95% confidence interval (CI) 64.6-90.4%) and 85.0% (73.9-96.1%) (p = 0.08) on CPI and VMI respectively and specificity 96.0% (91.4-100.0%) and 94.6% (89.4-99.7%) (p = 0.32). CONCLUSIONS Diagnostic performance of the AI algorithm that was trained on CPI did not drop on VMI, which is reassuring for its use in clinical practice. CLINICAL RELEVANCE STATEMENT A commercially available AI algorithm, trained on conventional polychromatic CTPA, could be safely used on virtual monochromatic images. This supports the sustainability of AI-aided detection of PE on CT despite ongoing technological advances in medical imaging, although monitoring in daily practice will remain important. KEY POINTS • Diagnostic accuracy of an AI algorithm trained on conventional polychromatic images to detect PE did not drop on virtual monochromatic images. • Our results are reassuring as innovations in hardware and reconstruction in CT are continuing, whilst commercial AI algorithms that are trained on older generation data enter healthcare.
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Affiliation(s)
- Eline Langius-Wiffen
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands.
| | - Ingrid M Nijholt
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
| | - Rogier A van Dijk
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
| | - Erwin de Boer
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
| | | | - Wouter B Veldhuis
- Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Centre Utrecht, Utrecht, the Netherlands
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
- Division of Imaging and Oncology, University Medical Centre Utrecht, Utrecht, The Netherlands
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Rambojun AM, Komber H, Rossdale J, Suntharalingam J, Rodrigues JCL, Ehrhardt MJ, Repetti A. Uncertainty quantification in computed tomography pulmonary angiography. PNAS NEXUS 2024; 3:pgad404. [PMID: 38737009 PMCID: PMC11087828 DOI: 10.1093/pnasnexus/pgad404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 10/26/2023] [Indexed: 05/14/2024]
Abstract
Computed tomography (CT) imaging of the thorax is widely used for the detection and monitoring of pulmonary embolism (PE). However, CT images can contain artifacts due to the acquisition or the processes involved in image reconstruction. Radiologists often have to distinguish between such artifacts and actual PEs. We provide a proof of concept in the form of a scalable hypothesis testing method for CT, to enable quantifying uncertainty of possible PEs. In particular, we introduce a Bayesian Framework to quantify the uncertainty of an observed compact structure that can be identified as a PE. We assess the ability of the method to operate under high-noise environments and with insufficient data.
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Affiliation(s)
- Adwaye M Rambojun
- Department of Mathematical Sciences, University of Bath, Bath BA2 7JU, UK
| | | | | | - Jay Suntharalingam
- Royal United Hospital, Bath BA1 3NG, UK
- Department of Life Sciences, University of Bath, Bath BA2 7JU, UK
| | | | | | - Audrey Repetti
- School of Engineering and Physical Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS, UK
- Maxwell Institute for Mathematical Sciences, Edinburgh EH8 9BT, UK
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Ishiwata T, Yasufuku K. Artificial intelligence in interventional pulmonology. Curr Opin Pulm Med 2024; 30:92-98. [PMID: 37916605 DOI: 10.1097/mcp.0000000000001024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
PURPOSE OF REVIEW In recent years, there has been remarkable progress in the field of artificial intelligence technology. Artificial intelligence applications have been extensively researched and actively implemented across various domains within healthcare. This study reviews the current state of artificial intelligence research in interventional pulmonology and engages in a discussion to comprehend its capabilities and implications. RECENT FINDINGS Deep learning, a subset of artificial intelligence, has found extensive applications in recent years, enabling highly accurate identification and labeling of bronchial segments solely from intraluminal bronchial images. Furthermore, research has explored the use of artificial intelligence for the analysis of endobronchial ultrasound images, achieving a high degree of accuracy in distinguishing between benign and malignant targets within ultrasound images. These advancements have become possible due to the increased computational power of modern systems and the utilization of vast datasets, facilitating detections and predictions with greater precision and speed. SUMMARY Artificial intelligence integration into interventional pulmonology has the potential to enhance diagnostic accuracy and patient safety, ultimately leading to improved patient outcomes. However, the clinical impacts of artificial intelligence enhanced procedures remain unassessed. Additional research is necessary to evaluate both the advantages and disadvantages of artificial intelligence in the field of interventional pulmonology.
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Affiliation(s)
- Tsukasa Ishiwata
- Division of Thoracic Surgery, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Langius-Wiffen E, de Jong PA, Mohamed Hoesein FA, Dekker L, van den Hoven AF, Nijholt IM, Boomsma MF, Veldhuis WB. Added value of an artificial intelligence algorithm in reducing the number of missed incidental acute pulmonary embolism in routine portal venous phase chest CT. Eur Radiol 2024; 34:367-373. [PMID: 37532902 DOI: 10.1007/s00330-023-10029-z] [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: 03/02/2023] [Revised: 06/06/2023] [Accepted: 06/14/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVES The purpose of this study was to evaluate the incremental value of artificial intelligence (AI) compared to the diagnostic accuracy of radiologists alone in detecting incidental acute pulmonary embolism (PE) on routine portal venous contrast-enhanced chest computed tomography (CT). METHODS CTs of 3089 consecutive patients referred to the radiology department for a routine contrast-enhanced chest CT between 27-5-2020 and 31-12-2020, were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The diagnostic performance of the AI was compared to the initial report. To determine the reference standard, discordant findings were independently evaluated by two readers. In case of disagreement, another experienced cardiothoracic radiologist with knowledge of the initial report and the AI output adjudicated. RESULTS The prevalence of acute incidental PE in the reference standard was 2.2% (67 of 3089 patients). In 25 cases, AI detected initially unreported PE. This included three cases concerning central/lobar PE. Sensitivity of the AI algorithm was significantly higher than the outcome of the initial report (respectively 95.5% vs. 62.7%, p < 0.001), whereas specificity was very high for both (respectively 99.6% vs 99.9%, p = 0.012). The AI algorithm only showed a slightly higher amount of false-positive findings (11 vs. 2), resulting in a significantly lower PPV (85.3% vs. 95.5%, p = 0.047). CONCLUSION The AI algorithm showed high diagnostic accuracy in diagnosing incidental PE, detecting an additional 25 cases of initially unreported PE, accounting for 37.3% of all positive cases. CLINICAL RELEVANCE STATEMENT Radiologist support from AI algorithms in daily practice can prevent missed incidental acute PE on routine chest CT, without a high burden of false-positive cases. KEY POINTS • Incidental pulmonary embolism is often missed by radiologists in non-diagnostic scans with suboptimal contrast opacification within the pulmonary trunk. • An artificial intelligence algorithm showed higher sensitivity detecting incidental pulmonary embolism on routine portal venous chest CT compared to the initial report. • Implementation of artificial intelligence support in routine daily practice will reduce the number of missed incidental pulmonary embolism.
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Affiliation(s)
- Eline Langius-Wiffen
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands.
| | - Pim A de Jong
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | | | - Lisette Dekker
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Andor F van den Hoven
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
- Department of Nuclear Medicine, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Ingrid M Nijholt
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital, Dr. Van Heesweg 2, 8025 AB, Zwolle, The Netherlands
- Division of Imaging and Oncology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Wouter B Veldhuis
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
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Jamin A, Hoffmann C, Mahe G, Bressollette L, Humeau-Heurtier A. Pulmonary embolism detection on venous thrombosis ultrasound images with bi-dimensional entropy measures: Preliminary results. Med Phys 2023; 50:7840-7851. [PMID: 37370233 DOI: 10.1002/mp.16568] [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: 03/24/2022] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Venous thromboembolism (VTE) is a common health issue. A clinical expression of VTE is a deep vein thrombosis (DVT) that may lead to pulmonary embolism (PE), a critical illness. When DVT is suspected, an ultrasound exam is performed. However, the characteristics of the clot observed on ultrasound images cannot be linked with the presence of PE. Computed tomography angiography is the gold standard to diagnose PE. Nevertheless, the latter technique is expensive and requires the use of contrast agents. PURPOSE In this article, we present an image processing method based on ultrasound images to determine whether PE is associated or not with lower limb DVT. In terms of medical equipment, this new approach (Doppler ultrasound image processing) is inexpensive and quite easy. METHODS With the aim to help medical doctors in detecting PE, we herein propose to process ultrasound images of patients with DVT. After a first step based on histogram equalization, the analysis procedure is based on the use of bi-dimensional entropy measures. Two different algorithms are tested: the bi-dimensional dispersion entropy (D i s p E n 2 D $DispEn_{2D}$ ) mesure and the bi-dimensional fuzzy entropy (F u z E n 2 D $FuzEn_{2D}$ ) mesure. Thirty-two patients (12 women and 20 men, 67.63 ± 16.19 years old), split into two groups (16 with and 16 without PE), compose our database of around 1490 ultrasound images (split into seven different sizes from 32× 32 px to 128 × 128 px). p-values, computed with the Mann-Whitney test, are used to determine if entropy values of the two groups are statistically significantly different. Receiver operating characteristic (ROC) curves are plotted and analyzed for the most significant cases to define if entropy values are able to discriminate the two groups. RESULTS p-values show that there are statistical differences betweenF u z E n 2 D $FuzEn_{2D}$ of patients with PE and patients without PE for 112× 112 px and 128× 128 px images. Area under the ROC curve (AUC) is higher than 0.7 (threshold for a fair test) for 112× 112 and 128× 128 images. The best value of AUC (0.72) is obtained for 112× 112 px images. CONCLUSIONS Bi-dimensional entropy measures applied to ultrasound images seem to offer encouraging perspectives for PE detection: our first experiment, on a small dataset, shows thatF u z E n 2 D $FuzEn_{2D}$ on 112× 112 px images is able to detect PE. The next step of our work will consist in testing this approach on a larger dataset and in integratingF u z E n 2 D $FuzEn_{2D}$ in a machine learning algorithm. Furthermore, this study could also contribute to PE risk prediction for patients with VTE.
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Affiliation(s)
| | - Clément Hoffmann
- Internal and Vascular Medicine and Pulmonology Department, CHU Brest, Brest, France
- INSERM U1304 Groupe d'Etude de la Thrombose de Bretagne Occidentale (GETBO), University Brest, Brest, France
- F-CRIN INNOVTE, Saint-Etienne, France
| | - Guillaume Mahe
- Vascular Medicine Department, Centre Hospitalier Universitaire (CHU) de Rennes, Rennes, France
- INSERM CIC1414 CIC Rennes, Rennes, France
- Université de Rennes 2, M2S-EA 7470, Rennes, France
| | - Luc Bressollette
- Internal and Vascular Medicine and Pulmonology Department, CHU Brest, Brest, France
- INSERM U1304 Groupe d'Etude de la Thrombose de Bretagne Occidentale (GETBO), University Brest, Brest, France
- F-CRIN INNOVTE, Saint-Etienne, France
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Saha PK, Nadeem SA, Comellas AP. A Survey on Artificial Intelligence in Pulmonary Imaging. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2023; 13:e1510. [PMID: 38249785 PMCID: PMC10796150 DOI: 10.1002/widm.1510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 06/21/2023] [Indexed: 01/23/2024]
Abstract
Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.
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Affiliation(s)
- Punam K Saha
- Departments of Radiology and Electrical and Computer Engineering, University of Iowa, Iowa City, IA, 52242
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Jabbarpour A, Ghassel S, Lang J, Leung E, Le Gal G, Klein R, Moulton E. The Past, Present, and Future Role of Artificial Intelligence in Ventilation/Perfusion Scintigraphy: A Systematic Review. Semin Nucl Med 2023; 53:752-765. [PMID: 37080822 DOI: 10.1053/j.semnuclmed.2023.03.002] [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: 02/20/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 04/22/2023]
Abstract
Ventilation-perfusion (V/Q) lung scans constitute one of the oldest nuclear medicine procedures, remain one of the few studies performed in the acute setting, and are amongst the few performed in the emergency setting. V/Q studies have witnessed a long fluctuation in adoption rates in parallel to continuous advances in image processing and computer vision techniques. This review provides an overview on the status of artificial intelligence (AI) in V/Q scintigraphy. To clearly assess the past, current, and future role of AI in V/Q scans, we conducted a systematic Ovid MEDLINE(R) literature search from 1946 to August 5, 2022 in addition to a manual search. The literature was reviewed and summarized in terms of methodologies and results for the various applications of AI to V/Q scans. The PRISMA guidelines were followed. Thirty-one publications fulfilled our search criteria and were grouped into two distinct categories: (1) disease diagnosis/detection (N = 22, 71.0%) and (2) cross-modality image translation into V/Q images (N = 9, 29.0%). Studies on disease diagnosis and detection relied heavily on shallow artificial neural networks for acute pulmonary embolism (PE) diagnosis and were primarily published between the mid-1990s and early 2000s. Recent applications almost exclusively regard image translation tasks from CT to ventilation or perfusion images with modern algorithms, such as convolutional neural networks, and were published between 2019 and 2022. AI research in V/Q scintigraphy for acute PE diagnosis in the mid-90s to early 2000s yielded promising results but has since been largely neglected and thus have yet to benefit from today's state-of-the art machine-learning techniques, such as deep neural networks. Recently, the main application of AI for V/Q has shifted towards generating synthetic ventilation and perfusion images from CT. There is therefore considerable potential to expand and modernize the use of real V/Q studies with state-of-the-art deep learning approaches, especially for workflow optimization and PE detection at both acute and chronic stages. We discuss future challenges and potential directions to compensate for the lag in this domain and enhance the value of this traditional nuclear medicine scan.
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Affiliation(s)
- Amir Jabbarpour
- Department of Physics, Carleton University, Ottawa, Ontario, Canada
| | - Siraj Ghassel
- Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada
| | - Jochen Lang
- Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada
| | - Eugene Leung
- Division of Nuclear Medicine and Molecular Imaging, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Grégoire Le Gal
- Division of Hematology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Ran Klein
- Department of Physics, Carleton University, Ottawa, Ontario, Canada; Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada; Division of Nuclear Medicine and Molecular Imaging, Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada; Department of Nuclear Medicine and Molecular Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada.
| | - Eric Moulton
- Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada; Jubilant DraxImage Inc., Kirkland, Quebec, Canada
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Dundamadappa SK. AI tools in Emergency Radiology reading room: a new era of Radiology. Emerg Radiol 2023; 30:647-657. [PMID: 37420044 DOI: 10.1007/s10140-023-02154-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/20/2023] [Indexed: 07/09/2023]
Abstract
Artificial intelligence tools in radiology practices have surged, with modules developed to target specific findings becoming increasingly prevalent and proving valuable in the daily emergency room radiology practice. The number of US Food and Drug Administration-cleared radiology-related algorithms has soared from just 10 in early 2017 to over 200 presently. This review will concentrate on the present utilization of AI tools in clinical ER radiology setting, including a brief discussion of the limitations of the technique. As radiologists, it is essential that we embrace this technology, comprehend its constraints, and use it to improve patient care.
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Akhoundi N, Sedghian S, Siami A, Yazdani nia I, Naseri Z, Ghadiri Asli SM, Hazara R. Does Adding the Pulmonary Infarction and Right Ventricle to Left Ventricle Diameter Ratio to the Qanadli Index (A Combined Qanadli Index) More Accurately, Predict Short-Term Mortality in Patients with Pulmonary Embolism? Indian J Radiol Imaging 2023; 33:478-483. [PMID: 37811186 PMCID: PMC10556326 DOI: 10.1055/s-0043-1769590] [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] [Indexed: 10/10/2023] Open
Abstract
Background The Qanadli index can be used to assess the severity of pulmonary arterial involvement in patients with acute pulmonary embolism. However, it seems that considering pulmonary infarction and right ventricle/left ventricle (RV/LV) ratio along with this index (called the combined Qanadli index) can provide a more accurate view of changes in cardiovascular parameters in these patients and help predict mortality in a better manner. In this regard, we evaluated the ability of the combined Qanadli index versus the Qanadli index in predicting short-term mortality in patients with pulmonary embolism. Methods This retrospective study enrolled 234 patients with acute pulmonary embolism. Patients were divided into two groups: those who expired in 30 days and who survived. Then they were evaluated by computed tomography angiography of pulmonary arteries. The RV/LV diameter ratio and also pulmonary artery obstruction index (PAOI) were calculated. The patient's computed tomography scans were reviewed for pulmonary infarction. By adding the RV/LV ratio and pulmonary infarction to PAOI, a new index called the modified Qanadli score was made. Univariable and multivariable logistic regression was done for finding predictors of mortality. Results Nine cases (40%) of patients in the mortality group and 42 (20%) of survivors had ischemic heart disease and the difference was significantly meaningful. The mean Qanadli index in the mortality group was 16.8 ± 8.45 and in survivors was 8.3 ± 4.2. By adding the pulmonary infarction score and PAOI score to RV/LV ratio score, the odds ratio (OR) for predicting mortality increased significantly to 13 and 16, respectively, which were significantly meaningful. Based on our findings, the highest OR for predicting short-term mortality was obtained through a combined Qanadli index (PAOI score + pulmonary infarction score + RV/LV score) that was 17 in univariable and 18 in multivariable logistic regression analysis ( p -value = 0.015). Conclusion The new combined Qanadli index has more ability than the Qanadli index and RV/LV ratio for predicting changes in cardiovascular parameters and short-term mortality in patients with pulmonary embolism.
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Affiliation(s)
- Neda Akhoundi
- Radiology Department, University of California San Diego, Hillcrest Hospital, San Diego, California, United States
| | - Sonia Sedghian
- Radiology Department, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Alireza Siami
- Department of Statistics, Biostatistical Analyzer, Amirkabir University of Technology, Tehran, Iran
| | - Iman Yazdani nia
- Radiology Department, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Zahra Naseri
- Radiology Department, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Reza Hazara
- Department of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
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