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Araújo ALD, Sperandio M, Calabrese G, Faria SS, Cardenas DAC, Martins MD, Saldivia-Siracusa C, Giraldo-Roldán D, Pedroso CM, Vargas PA, Lopes MA, Santos-Silva AR, Kowalski LP, Moraes MC. Artificial intelligence in healthcare applications targeting cancer diagnosis-part I: data structure, preprocessing and data organization. Oral Surg Oral Med Oral Pathol Oral Radiol 2025:S2212-4403(25)00005-7. [PMID: 39893121 DOI: 10.1016/j.oooo.2025.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2024] [Revised: 12/12/2024] [Accepted: 01/03/2025] [Indexed: 02/04/2025]
Abstract
BACKGROUND Machine learning techniques hold significant potential to support the diagnosis and prognosis of diseases. However, the success of these approaches is heavily dependent on rigorous data acquisition, preprocessing and data organization. METHODS This article reviews the literature to evaluate key factors in dataset construction, focusing on data structure, preprocessing, and data organization, particularly in the context of imaging data. RESULTS The main issues with data construction when dealing with medical applications are noise (incorrect or irrelevant data), sparsity/ limited availability, representativeness/variability, and data imbalance (uneven class distribution).While preprocessing steps prepare the data to be suitable for the models, data organization focuses in improving data arranging to increase the model performance. Additionally, the impact of CNN complexity in processing balanced, imbalanced, and complex datasets shows that complex CNNs are not always the optimal choice for every classification problem. CONCLUSION By integrating knowledge from Health Sciences and Biomedical Engineering, we aim to enhance healthcare professionals' understanding of machine learning for image analysis in Oral Medicine and Pathology. This encourages their involvement in patient recruitment and data acquisition, broadening their roles and significantly contributing to the creation of well-characterized datasets for future research and applications.
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Affiliation(s)
- Anna Luíza Damaceno Araújo
- Head and Neck Surgery Department, University of São Paulo Medical School, São Paulo, State of São Paulo, Brazil; Hospital Israelita Albert Einstein, São Paulo, Brazil.
| | - Marcelo Sperandio
- Department of Oral Medicine and Pathology, Faculdade São Leopoldo Mandic, Research Institute, Campinas, São Paulo, Brazil
| | - Giovanna Calabrese
- Institute of Science and Technology (ICT-UNIFESP), Federal University of São Paulo, São José dos Campos, São Paulo, Brazil
| | - Sarah S Faria
- Institute of Science and Technology (ICT-UNIFESP), Federal University of São Paulo, São José dos Campos, São Paulo, Brazil
| | - Diego Armando Cardona Cardenas
- Institute of Science and Technology (ICT-UNIFESP), Federal University of São Paulo, São José dos Campos, São Paulo, Brazil; Heart Institute, University of São Paulo, São Paulo, State of São Paulo, Brazil
| | - Manoela Domingues Martins
- Department of Oral Pathology, School of Dentistry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Cristina Saldivia-Siracusa
- Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Daniela Giraldo-Roldán
- Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Caique Mariano Pedroso
- Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Pablo Agustin Vargas
- Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Marcio Ajudarte Lopes
- Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Alan Roger Santos-Silva
- Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil
| | - Luiz Paulo Kowalski
- Head and Neck Surgery Department, University of São Paulo Medical School, São Paulo, State of São Paulo, Brazil; Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo, State of São Paulo, Brazil
| | - Matheus Cardoso Moraes
- Institute of Science and Technology (ICT-UNIFESP), Federal University of São Paulo, São José dos Campos, São Paulo, Brazil
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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024; 15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open
Abstract
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
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Affiliation(s)
- Mahdi S. Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N. Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
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Long C, Subburam D, Lowe K, Dos Santos A, Zhang J, Hwang S, Saduka N, Horev Y, Su T, Côté DWJ, Wright ED. ChatENT: Augmented Large Language Model for Expert Knowledge Retrieval in Otolaryngology-Head and Neck Surgery. Otolaryngol Head Neck Surg 2024; 171:1042-1051. [PMID: 38895862 DOI: 10.1002/ohn.864] [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: 12/11/2023] [Revised: 05/05/2024] [Accepted: 05/09/2024] [Indexed: 06/21/2024]
Abstract
OBJECTIVE The recent surge in popularity of large language models (LLMs), such as ChatGPT, has showcased their proficiency in medical examinations and potential applications in health care. However, LLMs possess inherent limitations, including inconsistent accuracy, specific prompting requirements, and the risk of generating harmful hallucinations. A domain-specific model might address these limitations effectively. STUDY DESIGN Developmental design. SETTING Virtual. METHODS Otolaryngology-head and neck surgery (OHNS) relevant data were systematically gathered from open-access Internet sources and indexed into a knowledge database. We leveraged Retrieval-Augmented Language Modeling to recall this information and utilized it for pretraining, which was then integrated into ChatGPT4.0, creating an OHNS-specific knowledge question & answer platform known as ChatENT. The model is further tested on different types of questions. RESULTS ChatENT showed enhanced performance in the analysis and interpretation of OHNS information, outperforming ChatGPT4.0 in both the Canadian Royal College OHNS sample examination questions challenge and the US board practice questions challenge, with a 58.4% and 26.0% error reduction, respectively. ChatENT generated fewer hallucinations and demonstrated greater consistency. CONCLUSION To the best of our knowledge, ChatENT is the first specialty-specific knowledge retrieval artificial intelligence in the medical field that utilizes the latest LLM. It appears to have considerable promise in areas such as medical education, patient education, and clinical decision support. The model has demonstrated the capacity to overcome the limitations of existing LLMs, thereby signaling a future of more precise, safe, and user-friendly applications in the realm of OHNS and other medical fields.
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Affiliation(s)
- Cai Long
- Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada
| | | | - Kayle Lowe
- Faculty of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - André Dos Santos
- Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Jessica Zhang
- Faculty of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Sang Hwang
- Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada
| | | | - Yoav Horev
- Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada
| | - Tao Su
- Copula AI, New York, New York, USA
| | - David W J Côté
- Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada
| | - Erin D Wright
- Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada
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Hori M, Jincho M, Hori T, Sekine H, Kato A, Miyazawa K, Kawai T. Automatic point detection on cephalograms using convolutional neural networks: A two-step method. Dent Mater J 2024; 43:701-710. [PMID: 39231691 DOI: 10.4012/dmj.2024-052] [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] [Indexed: 09/06/2024]
Abstract
This project aimed to develop an artificial intelligence program tailored for cephalometric images. The program employs a convolutional neural network with 6 convolutional layers and 2 affine layers. It identifies 18 key points on the skull to compute various angles essential for diagnosis. Utilizing a custom-built desktop computer with a moderately priced graphics processing unit, cephalogram images were resized to 800×800 pixels. Training data comprised 833 images, augmented 100 times; an additional 179 images were used for testing. Due to the complexity of training with full-size images, training was divided into two steps. The first step reduced images to 128×128 pixels, recognizing all 18 points. In the second step, 100×100 pixels blocks were extracted from original images for individual point training. The program then measured six angles, achieving an average error of 3.1 pixels for the 18 points, with SNA and SNB angles showing an average difference of less than 1°.
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Affiliation(s)
- Miki Hori
- Department of Dental Materials Science, School of Dentistry, Aichi Gakuin University
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
| | - Makoto Jincho
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
| | - Tadasuke Hori
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
| | - Hironao Sekine
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
| | - Akiko Kato
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
- Department of Oral Anatomy, School of Dentistry, Aichi Gakuin University
| | - Ken Miyazawa
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
- Department of Orthodontics, School of Dentistry, Aichi Gakuin University
| | - Tatsushi Kawai
- Department of Dental Materials Science, School of Dentistry, Aichi Gakuin University
- Center for Advanced Oral Science, Graduate School of Dentistry, Aichi Gakuin University
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Yang X, Zhang R, Yang Y, Zhang Y, Chen K. PathEX: Make good choice for whole slide image extraction. PLoS One 2024; 19:e0304702. [PMID: 39208135 PMCID: PMC11361590 DOI: 10.1371/journal.pone.0304702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 05/17/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND The tile-based approach has been widely used for slide-level predictions in whole slide image (WSI) analysis. However, the irregular shapes and variable dimensions of tumor regions pose challenges for the process. To address this issue, we proposed PathEX, a framework that integrates intersection over tile (IoT) and background over tile (BoT) algorithms to extract tile images around boundaries of annotated regions while excluding the blank tile images within these regions. METHODS We developed PathEX, which incorporated IoT and BoT into tile extraction, for training a classification model in CAM (239 WSIs) and PAIP (40 WSIs) datasets. By adjusting the IoT and BoT parameters, we generated eight training sets and corresponding models for each dataset. The performance of PathEX was assessed on the testing set comprising 13,076 tile images from 48 WSIs of CAM dataset and 6,391 tile images from 10 WSIs of PAIP dataset. RESULTS PathEX could extract tile images around boundaries of annotated region differently by adjusting the IoT parameter, while exclusion of blank tile images within annotated regions achieved by setting the BoT parameter. As adjusting IoT from 0.1 to 1.0, and 1-BoT from 0.0 to 0.5, we got 8 train sets. Experimentation revealed that set C demonstrates potential as the most optimal candidate. Nevertheless, a combination of IoT values ranging from 0.2 to 0.5 and 1-BoT values ranging from 0.2 to 0.5 also yielded favorable outcomes. CONCLUSIONS In this study, we proposed PathEX, a framework that integrates IoT and BoT algorithms for tile image extraction at the boundaries of annotated regions while excluding blank tiles within these regions. Researchers can conveniently set the thresholds for IoT and BoT to facilitate tile image extraction in their own studies. The insights gained from this research provide valuable guidance for tile image extraction in digital pathology applications.
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Affiliation(s)
- Xinda Yang
- Renmin University of China School of Information, Beijing, P.R. China
| | - Ranze Zhang
- Breast Tumor Center, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Breast Tumor Center, Sun Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yuan Yang
- Department of Research and Development, Health Data (Beijing) Technology Co., Ltd, Guangzhou, Guangdong, P.R. China
| | - Yu Zhang
- Renmin University of China School of Information, Beijing, P.R. China
| | - Kai Chen
- Breast Tumor Center, Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Breast Tumor Center, Sun Yat-sen Breast Tumor Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
- Artificial Intelligence Lab, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
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Yuan L, Shen Z, Shan Y, Zhu J, Wang Q, Lu Y, Shi H. Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis. Front Oncol 2024; 14:1432212. [PMID: 39040448 PMCID: PMC11260632 DOI: 10.3389/fonc.2024.1432212] [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: 05/13/2024] [Accepted: 06/19/2024] [Indexed: 07/24/2024] Open
Abstract
Background Pathomics has emerged as a promising biomarker that could facilitate personalized immunotherapy in lung cancer. It is essential to elucidate the global research trends and emerging prospects in this domain. Methods The annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools. Results A total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms "deep learning", "tumor microenvironment", "biomarkers", "image analysis", "immunotherapy", and "survival prediction", etc. are hot keywords in this field. Conclusion In future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.
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Affiliation(s)
- Lei Yuan
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Zhiming Shen
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Yibo Shan
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Jianwei Zhu
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Qi Wang
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Yi Lu
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
| | - Hongcan Shi
- Department of Thoracic Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, China
- Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Treatment of Senile Diseases, Yangzhou University, Yangzhou, China
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Jain E, Patel A, Parwani AV, Shafi S, Brar Z, Sharma S, Mohanty SK. Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives. Int J Surg Pathol 2024; 32:433-448. [PMID: 37437093 DOI: 10.1177/10668969231185089] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
Background. Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.
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Affiliation(s)
- Ekta Jain
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Ankush Patel
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Anil V Parwani
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Saba Shafi
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Zoya Brar
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Shivani Sharma
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Sambit K Mohanty
- Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
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Dougan J, Patel N, Bardarov S. A Comparison of Diagnostic and Immunohistochemical Workup and Literature Review Capabilities of Online Artificial Intelligence Assistance Models in Pathology. Cureus 2024; 16:e61075. [PMID: 38915984 PMCID: PMC11196119 DOI: 10.7759/cureus.61075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/23/2024] [Indexed: 06/26/2024] Open
Abstract
Artificial intelligence (AI) is a suite of technologies that enables computers to learn and interpret information like human cognition. It has found applications across various fields, including healthcare, agriculture, astronomy, navigation, and robotics. Within healthcare, AI has the potential to enhance diagnostic accuracy, facilitate drug research, and automate patient experiences. This comparative study focuses on the proficiency of AI in generating accurate differential diagnoses in the field of pathology. Six medical vignettes were crafted, and each scenario was then input into three different AI platforms. The pathologist reviewed and determined the most accurate AI model.
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Affiliation(s)
- Johnika Dougan
- Pathology and Laboratory Medicine, St. George's University School of Medicine, St. George's, GRD
| | - Netra Patel
- Pathology and Laboratory Medicine, American University of Antigua, Antigua, USA
| | - Svetoslav Bardarov
- Pathology and Laboratory Medicine, Richmond University Medical Center, Staten Island, USA
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Reigle J, Lopez-Nunez O, Drysdale E, Abuquteish D, Liu X, Putra J, Erdman L, Griffiths AM, Prasath S, Siddiqui I, Dhaliwal J. Using Deep Learning to Automate Eosinophil Counting in Pediatric Ulcerative Colitis Histopathological Images. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.03.24305251. [PMID: 38633803 PMCID: PMC11023647 DOI: 10.1101/2024.04.03.24305251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Background Accurate identification of inflammatory cells from mucosal histopathology images is important in diagnosing ulcerative colitis. The identification of eosinophils in the colonic mucosa has been associated with disease course. Cell counting is not only time-consuming but can also be subjective to human biases. In this study we developed an automatic eosinophilic cell counting tool from mucosal histopathology images, using deep learning. Method Four pediatric IBD pathologists from two North American pediatric hospitals annotated 530 crops from 143 standard-of-care hematoxylin and eosin (H & E) rectal mucosal biopsies. A 305/75 split was used for training/validation to develop and optimize a U-Net based deep learning model, and 150 crops were used as a test set. The U-Net model was then compared to SAU-Net, a state-of-the-art U-Net variant. We undertook post-processing steps, namely, (1) the pixel-level probability threshold, (2) the minimum number of clustered pixels to designate a cell, and (3) the connectivity. Experiments were run to optimize model parameters using AUROC and cross-entropy loss as the performance metrics. Results The F1-score was 0.86 (95%CI:0.79-0.91) (Precision: 0.77 (95%CI:0.70-0.83), Recall: 0.96 (95%CI:0.93-0.99)) to identify eosinophils as compared to an F1-score of 0.2 (95%CI:0.13-0.26) for SAU-Net (Precision: 0.38 (95%CI:0.31-0.46), Recall: 0.13 (95%CI:0.08-0.19)). The inter-rater reliability was 0.96 (95%CI:0.93-0.97). The correlation between two pathologists and the algorithm was 0.89 (95%CI:0.82-0.94) and 0.88 (95%CI:0.80-0.94) respectively. Conclusion Our results indicate that deep learning-based automated eosinophilic cell counting can obtain a robust level of accuracy with a high degree of concordance with manual expert annotations.
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10
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Shah SSH, Elmorsy E, Othman RQA, Syed A, Armaghan SU, Khalid Bokhari SU, Elmorsy ME, Bawadekji A. The Evaluation of Artificial Intelligence Technology for the Differentiation of Fresh Human Blood Cells From Other Species' Blood in the Investigation of Crime Scenes. Cureus 2024; 16:e58496. [PMID: 38765447 PMCID: PMC11101600 DOI: 10.7759/cureus.58496] [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: 04/17/2024] [Indexed: 05/22/2024] Open
Abstract
OBJECTIVES The current study used the deep machine learning approach to differentiate human blood specimens from cow, goat, and chicken blood stains based on cell morphology. METHODS A total of 1,955 known Giemsa-stained digitized images were acquired from the blood of humans, cows, goats, and chickens. To train the deep learning models, the well-known VGG16, Resnet18, and Resnet34 algorithms were used. Based on the image analysis, confusion matrices were generated. RESULTS Findings showed that the F1 score for the chicken, cow, goat, and human classes were all equal to 1.0 for each of the three algorithms. The Matthews correlation coefficient (MCC) was 1 for chickens, cows, and humans in all three algorithms, while the MCC score was 0.989 for goats by ResNet18, and it was 0.994 for both ResNet34 and VGG16 algorithms. The three algorithms showed 100% sensitivity, specificity, and positive and negative predictive values for the human, cow, and chicken cells. For the goat cells, the data showed 100% sensitivity and negative predictive values with specificity and positive predictive values ranging from 98.5% to 99.6%. CONCLUSION These data showed the importance of deep learning as a potential tool for the differentiation of the species of origin of fresh crime scene blood stains.
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Affiliation(s)
| | - Ekramy Elmorsy
- Department of Pathology, Northern Border University, Arar, SAU
| | | | - Asmara Syed
- Department of Pathology, Northern Border University, Arar, SAU
| | - Syed Umar Armaghan
- Department of Research & Development - Robotic Section, Idrak AI Pvt. Ltd., Islamabad, PAK
| | | | - Mahmoud E Elmorsy
- Department of Computer Engineering, King Fahd University of Petroleum and Minerals, Dhahran, SAU
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11
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Chou RH, Hsu BWY, Yu CL, Chen TY, Ou SM, Lee KH, Tseng VS, Huang PH, Tarng DC. Machine-learning models are superior to severity scoring systems for the prediction of the mortality of critically ill patients in a tertiary medical center. J Chin Med Assoc 2024; 87:369-376. [PMID: 38334988 DOI: 10.1097/jcma.0000000000001066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Intensive care unit (ICU) mortality prediction helps to guide therapeutic decision making for critically ill patients. Several scoring systems based on statistical techniques have been developed for this purpose. In this study, we developed a machine-learning model to predict patient mortality in the very early stage of ICU admission. METHODS This study was performed with data from all patients admitted to the intensive care units of a tertiary medical center in Taiwan from 2009 to 2018. The patients' comorbidities, co-medications, vital signs, and laboratory data on the day of ICU admission were obtained from electronic medical records. We constructed random forest and extreme gradient boosting (XGBoost) models to predict ICU mortality, and compared their performance with that of traditional scoring systems. RESULTS Data from 12,377 patients was allocated to training (n = 9901) and testing (n = 2476) datasets. The median patient age was 70.0 years; 9210 (74.41%) patients were under mechanical ventilation in the ICU. The areas under receiver operating characteristic curves for the random forest and XGBoost models (0.876 and 0.880, respectively) were larger than those for the Acute Physiology and Chronic Health Evaluation II score (0.738), Sequential Organ Failure Assessment score (0.747), and Simplified Acute Physiology Score II (0.743). The fraction of inspired oxygen on ICU admission was the most important predictive feature across all models. CONCLUSION The XGBoost model most accurately predicted ICU mortality and was superior to traditional scoring systems. Our results highlight the utility of machine learning for ICU mortality prediction in the Asian population.
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Affiliation(s)
- Ruey-Hsing Chou
- Department of Critical Care Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Benny Wei-Yun Hsu
- Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC
| | - Chun-Lin Yu
- Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC
| | - Tai-Yuan Chen
- Institute of Computer Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC
| | - Shuo-Ming Ou
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC
| | - Kuo-Hua Lee
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC
| | - Vincent S Tseng
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC
| | - Po-Hsun Huang
- Department of Critical Care Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Cardiovascular Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Der-Cherng Tarng
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Division of Nephrology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan, ROC
- Department and Institute of Physiology, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
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12
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Abdul NS, Shivakumar GC, Sangappa SB, Di Blasio M, Crimi S, Cicciù M, Minervini G. Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis. BMC Oral Health 2024; 24:122. [PMID: 38263027 PMCID: PMC10804575 DOI: 10.1186/s12903-023-03533-7] [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: 07/24/2023] [Accepted: 10/11/2023] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Since AI algorithms can analyze patient data, medical records, and imaging results to suggest treatment plans and predict outcomes, they have the potential to support pathologists and clinicians in the diagnosis and treatment of oral and maxillofacial pathologies, just like every other area of life in which it is being used. The goal of the current study was to examine all of the trends being investigated in the area of oral and maxillofacial pathology where AI has been possibly involved in helping practitioners. METHODS We started by defining the important terms in our investigation's subject matter. Following that, relevant databases like PubMed, Scopus, and Web of Science were searched using keywords and synonyms for each concept, such as "machine learning," "diagnosis," "treatment planning," "image analysis," "predictive modelling," and "patient monitoring." For more papers and sources, Google Scholar was also used. RESULTS The majority of the 9 studies that were chosen were on how AI can be utilized to diagnose malignant tumors of the oral cavity. AI was especially helpful in creating prediction models that aided pathologists and clinicians in foreseeing the development of oral and maxillofacial pathology in specific patients. Additionally, predictive models accurately identified patients who have a high risk of developing oral cancer as well as the likelihood of the disease returning after treatment. CONCLUSIONS In the field of oral and maxillofacial pathology, AI has the potential to enhance diagnostic precision, personalize care, and ultimately improve patient outcomes. The development and application of AI in healthcare, however, necessitates careful consideration of ethical, legal, and regulatory challenges. Additionally, because AI is still a relatively new technology, caution must be taken when applying it to this industry.
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Affiliation(s)
- Nishath Sayed Abdul
- Department of OMFS & Diagnostic Sciences, College of Dentistry, Riyadh Elm, University, Riyadh, Saudi Arabia
| | - Ganiga Channaiah Shivakumar
- Department of Oral Medicine and Radiology, People's College of Dental Sciences and Research Centre, People's University, Bhopal, 462037, India.
| | - Sunila Bukanakere Sangappa
- Department of Prosthodontics and Crown & Bridge, JSS Dental College and Hospital, JSS Academy of Higher Education and Research, Mysuru, Karnataka, India
| | - Marco Di Blasio
- Department of Medicine and Surgery, University Center of Dentistry, University of Parma, 43126, Parma, Italy.
| | - Salvatore Crimi
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, CT, Italy
| | - Marco Cicciù
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, CT, Italy
| | - Giuseppe Minervini
- Saveetha Dental College & Hospitals, Saveetha Institute of Medical & Technical Sciences, Saveetha University, Chennai, India.
- Multidisciplinary Department of Medical-Surgical and Odontostomatological Specialties, University of Campania "Luigi Vanvitelli", Naples, Italy.
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13
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Szeremeta M, Janica J, Niemcunowicz-Janica A. Artificial intelligence in forensic medicine and related sciences - selected issues. ARCHIVES OF FORENSIC MEDICINE AND CRIMINOLOGY 2024; 74:64-76. [PMID: 39450596 DOI: 10.4467/16891716amsik.24.005.19650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/16/2024] [Indexed: 10/26/2024] Open
Abstract
Aim The aim of the work is to provide an overview of the potential application of artificial intelligence in forensic medicine and related sciences, and to identify concerns related to providing medico-legal opinions and legal liability in cases in which possible harm in terms of diagnosis and/or treatment is likely to occur when using an advanced system of computer-based information processing and analysis. Material and methods The material for the study comprised scientific literature related to the issue of artificial intelligence in forensic medicine and related sciences. For this purpose, Google Scholar, PubMed and ScienceDirect databases were searched. To identify useful articles, such terms as "artificial intelligence," "deep learning," "machine learning," "forensic medicine," "legal medicine," "forensic pathology" and "medicine" were used. In some cases, articles were identified based on the semantic proximity of the introduced terms. Conclusions Dynamic development of the computing power and the ability of artificial intelligence to analyze vast data volumes made it possible to transfer artificial intelligence methods to forensic medicine and related sciences. Artificial intelligence has numerous applications in forensic medicine and related sciences and can be helpful in thanatology, forensic traumatology, post-mortem identification examinations, as well as post-mortem microscopic and toxicological diagnostics. Analyzing the legal and medico-legal aspects, artificial intelligence in medicine should be treated as an auxiliary tool, whereas the final diagnostic and therapeutic decisions and the extent to which they are implemented should be the responsibility of humans.
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Affiliation(s)
- Michał Szeremeta
- Department of Forensic Medicine, Medical University of Białystok, Poland
| | - Julia Janica
- Student's Scientific Group at the Department of Forensic Medicine, Poland
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14
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Malik S, Zaheer S. ChatGPT as an aid for pathological diagnosis of cancer. Pathol Res Pract 2024; 253:154989. [PMID: 38056135 DOI: 10.1016/j.prp.2023.154989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/08/2023]
Abstract
Diagnostic workup of cancer patients is highly reliant on the science of pathology using cytopathology, histopathology, and other ancillary techniques like immunohistochemistry and molecular cytogenetics. Data processing and learning by means of artificial intelligence (AI) has become a spearhead for the advancement of medicine, with pathology and laboratory medicine being no exceptions. ChatGPT, an artificial intelligence (AI)-based chatbot, that was recently launched by OpenAI, is currently a talk of the town, and its role in cancer diagnosis is also being explored meticulously. Pathology workflow by integration of digital slides, implementation of advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enables effective integration, assimilation, and utilization of knowledge that is beyond human limits and boundaries. Despite of it's numerous advantages in the pathological diagnosis of cancer, it comes with several challenges like integration of digital slides with input language parameters, problems of bias, and legal issues which have to be addressed and worked up soon so that we as a pathologists diagnosing malignancies are on the same band wagon and don't miss the train.
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Affiliation(s)
- Shaivy Malik
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India
| | - Sufian Zaheer
- Department of Pathology, Vardhman Mahavir Medical College and Safdarjung Hospital, New Delhi, India.
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15
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Rudmann DG, Bertrand L, Zuraw A, Deiters J, Staup M, Rivenson Y, Kuklyte J. Building a nonclinical pathology laboratory of the future for pharmaceutical research excellence. Drug Discov Today 2023; 28:103747. [PMID: 37598916 DOI: 10.1016/j.drudis.2023.103747] [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: 07/12/2023] [Revised: 08/02/2023] [Accepted: 08/14/2023] [Indexed: 08/22/2023]
Abstract
We describe a roadmap for a fully digital artificial intelligence (AI)-augmented nonclinical pathology laboratory across three continents. Underpinning the design are Good Laboratory Practice (GLP)-validated laboratory information management systems (LIMS), whole slide-scanners (WSS), image management systems (IMS), and a digital microscope intended for use by the nonclinical pathologist. Digital diagnostics are supported by tools that include AI-based virtual staining and deep learning-based decision support. Implemented during the COVID-19 pandemic, the initial digitized workflow largely mitigated disruption of pivotal nonclinical studies required to support pharmaceutical clinical testing. We believe that this digital transformation of our nonclinical pathology laboratories will promote efficiency and innovation in the future and enhance the quality and speed of drug development decision making.
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Affiliation(s)
- D G Rudmann
- Charles River Laboratories, Digital Toxicologic Pathology, Discovery and Safety Assessment, Wilmington, DE, USA.
| | - L Bertrand
- Charles River Laboratories, Digital Toxicologic Pathology, Discovery and Safety Assessment, Wilmington, DE, USA
| | - A Zuraw
- Charles River Laboratories, Digital Toxicologic Pathology, Discovery and Safety Assessment, Wilmington, DE, USA
| | - J Deiters
- Charles River Laboratories, Digital Toxicologic Pathology, Discovery and Safety Assessment, Wilmington, DE, USA
| | - M Staup
- Charles River Laboratories, Digital Toxicologic Pathology, Discovery and Safety Assessment, Wilmington, DE, USA
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16
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Neary-Zajiczek L, Beresna L, Razavi B, Pawar V, Shaw M, Stoyanov D. Minimum resolution requirements of digital pathology images for accurate classification. Med Image Anal 2023; 89:102891. [PMID: 37536022 DOI: 10.1016/j.media.2023.102891] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/22/2023] [Accepted: 07/06/2023] [Indexed: 08/05/2023]
Abstract
Digitization of pathology has been proposed as an essential mitigation strategy for the severe staffing crisis facing most pathology departments. Despite its benefits, several barriers have prevented widespread adoption of digital workflows, including cost and pathologist reluctance due to subjective image quality concerns. In this work, we quantitatively determine the minimum image quality requirements for binary classification of histopathology images of breast tissue in terms of spatial and sampling resolution. We train an ensemble of deep learning classifier models on publicly available datasets to obtain a baseline accuracy and computationally degrade these images according to our derived theoretical model to identify the minimum resolution necessary for acceptable diagnostic accuracy. Our results show that images can be degraded significantly below the resolution of most commercial whole-slide imaging systems while maintaining reasonable accuracy, demonstrating that macroscopic features are sufficient for binary classification of stained breast tissue. A rapid low-cost imaging system capable of identifying healthy tissue not requiring human assessment could serve as a triage system for reducing caseloads and alleviating the significant strain on the current workforce.
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Affiliation(s)
- Lydia Neary-Zajiczek
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
| | - Linas Beresna
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Benjamin Razavi
- University College London Medical School, 74 Huntley Street, London, WC1E 6BT, United Kingdom
| | - Vijay Pawar
- Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Michael Shaw
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom; National Physical Laboratory, Hampton Road, Teddington, TW11 0LW, United Kingdom
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, Charles Bell House, 43-45 Foley Street, Fitzrovia, London, W1W 7TS, United Kingdom; Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom
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17
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Patel P, Harmon S, Iseman R, Ludkowski O, Auman H, Hawley S, Newcomb LF, Lin DW, Nelson PS, Feng Z, Boyer HD, Tretiakova MS, True LD, Vakar-Lopez F, Carroll PR, Cooperberg MR, Chan E, Simko J, Fazli L, Gleave M, Hurtado-Coll A, Thompson IM, Troyer D, McKenney JK, Wei W, Choyke PL, Bratslavsky G, Turkbey B, Siemens DR, Squire J, Peng YP, Brooks JD, Jamaspishvili T. Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study. Mod Pathol 2023; 36:100241. [PMID: 37343766 PMCID: PMC10592257 DOI: 10.1016/j.modpat.2023.100241] [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: 12/13/2022] [Revised: 05/23/2023] [Accepted: 06/06/2023] [Indexed: 06/23/2023]
Abstract
Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.
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Affiliation(s)
- Palak Patel
- Department of Cell Biology at The Arthur and Sonia Labatt Brain Tumour Research Centre at the Hospital for Sick Children, Toronto, Ontario, Canada
| | - Stephanie Harmon
- Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland; Artificial Intelligence Resource, National Cancer Institute, Bethesda, Maryland
| | - Rachael Iseman
- Division of Cancer Biology and Genetics, Queen's University, Kingston, Ontario, Canada
| | - Olga Ludkowski
- University Health Network, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | | | | | - Lisa F Newcomb
- Department of Urology, University of Washington Medical Center, Seattle, Washington
| | - Daniel W Lin
- Department of Urology, University of Washington Medical Center, Seattle, Washington
| | - Peter S Nelson
- Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Ziding Feng
- Program of Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Hilary D Boyer
- Program of Biostatistics and Biomathematics, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Maria S Tretiakova
- Department of Pathology, University of Washington Medical Center, Seattle, Washington
| | - Larry D True
- Department of Pathology, University of Washington Medical Center, Seattle, Washington
| | - Funda Vakar-Lopez
- Department of Pathology, University of Washington Medical Center, Seattle, Washington
| | - Peter R Carroll
- Department of Urology, University of California San Francisco and Helen Diller Family, Comprehensive Cancer Center, San Francisco, California
| | - Matthew R Cooperberg
- Department of Urology, University of California San Francisco and Helen Diller Family, Comprehensive Cancer Center, San Francisco, California
| | - Emily Chan
- Department of Urology, University of California San Francisco and Helen Diller Family, Comprehensive Cancer Center, San Francisco, California
| | - Jeff Simko
- Department of Urology, University of California San Francisco and Helen Diller Family, Comprehensive Cancer Center, San Francisco, California; Department of Pathology, University of California San Francisco, San Francisco, California
| | - Ladan Fazli
- The Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Martin Gleave
- The Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Antonio Hurtado-Coll
- The Vancouver Prostate Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Dean Troyer
- Department of Pathology, Eastern Virginia Medical School, Norfolk, Virginia; Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia
| | | | - Wei Wei
- Department of Pathology, Cleveland Clinic, Cleveland, Ohio
| | - Peter L Choyke
- Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland
| | | | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, Bethesda, Maryland; Artificial Intelligence Resource, National Cancer Institute, Bethesda, Maryland
| | - D Robert Siemens
- Department of Urology, Queen's University, Kingston, Ontario, Canada
| | - Jeremy Squire
- Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Yingwei P Peng
- Department of Public Health Sciences, Queen's University, Kingston, Ontario, Canada; Department of Mathematics and Statistics, Queen's University, Kingston, Ontario, Canada
| | - James D Brooks
- Department of Urology, Stanford University Medical Center, Stanford, California
| | - Tamara Jamaspishvili
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada; Department of Pathology and Molecular Medicine, SUNY Upstate Medical University, Syracuse, New York.
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18
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Singhal I, Kaur G, Neefs D, Pathak A. A Literature Review of the Future of Oral Medicine and Radiology, Oral Pathology, and Oral Surgery in the Hands of Technology. Cureus 2023; 15:e45804. [PMID: 37876387 PMCID: PMC10591112 DOI: 10.7759/cureus.45804] [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: 09/22/2023] [Indexed: 10/26/2023] Open
Abstract
In the realm of dentistry, a myriad of technological advancements, including teledentistry, virtual reality (VR), artificial intelligence (AI), and three-dimensional printing, have been extensively embraced and rigorously evaluated, consistently demonstrating their remarkable effectiveness. These innovations have ushered in a transformative era in dentistry, impacting every facet of the field. They encompass activities ranging from the diagnosis and exploration of oral health conditions to the formulation of treatment plans, execution of surgical procedures, fabrication of prosthetics, and even assistance in patient distraction, prognosis, and disease prevention. Despite the significant strides already taken, the relentless pursuit of new horizons fueled by human curiosity remains unabated. The future landscape of dentistry holds the promise of sweeping changes, notably characterized by enhanced accessibility to dental care and reduced treatment durations. In this comprehensive review article, we delve into the pivotal roles played by AI, VR, augmented reality, mixed reality, and extended reality within the realm of dentistry, with a particular emphasis on their applications in oral medicine, oral radiology, oral surgery, and oral pathology. These technologies represent just a fraction of the technological arsenal currently harnessed in the field of dentistry. A thorough comprehension of their advantages and limitations is imperative for informed decision-making in their utilization.
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Affiliation(s)
- Ishita Singhal
- Oral Pathology and Microbiology and Forensic Odontology, Shree Guru Gobind Singh Tricentenary (SGT) University, Gurugram, IND
| | - Geetpriya Kaur
- Oral Pathology and Microbiology, Paradise Diagnostics, New Delhi, IND
| | - Dirk Neefs
- Dentistry, Dierick Dental Care, Antwerp, BEL
| | - Aparna Pathak
- Oral Pathology, Paradise Diagnostics, New Delhi, IND
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19
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Schüffler P, Steiger K, Weichert W. How to use AI in pathology. Genes Chromosomes Cancer 2023; 62:564-567. [PMID: 37254901 DOI: 10.1002/gcc.23178] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/15/2023] [Accepted: 05/20/2023] [Indexed: 06/01/2023] Open
Abstract
AI plays an important role in pathology, both in clinical practice supporting pathologists in their daily work, and in research discovering novel biomarkers for improved patient care. Still, AI is in its starting phase, and many pathology labs still need to transition to a digital workflow to be able to enjoy the benefits of AI. In this perspective, we explain the major benefits of AI in pathology, highlight key requirements that need to be met and example how to use it in a typical workflow.
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Affiliation(s)
- Peter Schüffler
- Institute of Pathology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- TUM School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
- Munich Data Science Institute, Technical University of Munich, Munich, Germany
| | - Katja Steiger
- Institute of Pathology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Wilko Weichert
- Institute of Pathology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
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20
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Gniadek T, Kang J, Theparee T, Krive J. Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine. Online J Public Health Inform 2023; 15:e50934. [PMID: 38046562 PMCID: PMC10689048 DOI: 10.2196/50934] [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: 03/30/2020] [Accepted: 02/27/2023] [Indexed: 12/05/2023] Open
Abstract
Artificial intelligence (AI) applied to medicine offers immense promise, in addition to safety and regulatory concerns. Traditional AI produces a core algorithm result, typically without a measure of statistical confidence or an explanation of its biological-theoretical basis. Efforts are underway to develop explainable AI (XAI) algorithms that not only produce a result but also an explanation to support that result. Here we present a framework for classifying XAI algorithms applied to clinical medicine: An algorithm's clinical scope is defined by whether the core algorithm output leads to observations (eg, tests, imaging, clinical evaluation), interventions (eg, procedures, medications), diagnoses, and prognostication. Explanations are classified by whether they provide empiric statistical information, association with a historical population or populations, or association with an established disease mechanism or mechanisms. XAI implementations can be classified based on whether algorithm training and validation took into account the actions of health care providers in response to the insights and explanations provided or whether training was performed using only the core algorithm output as the end point. Finally, communication modalities used to convey an XAI explanation can be used to classify algorithms and may affect clinical outcomes. This framework can be used when designing, evaluating, and comparing XAI algorithms applied to medicine.
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Affiliation(s)
- Thomas Gniadek
- Department of Pathology and Laboratory Medicine NorthShore University Health System Evanston, IL United States
| | - Jason Kang
- Department of Pathology and Laboratory Medicine NorthShore University Health System Evanston, IL United States
| | - Talent Theparee
- Department of Pathology and Laboratory Medicine NorthShore University Health System Evanston, IL United States
| | - Jacob Krive
- Department of Biomedical and Health Information Sciences University of Illinois at Chicago Chicago, IL United States
- Department of Health Information Technology NorthShore University Health System Evanston, IL United States
- Department of Health Informatics Dr Kiran C Patel School of Osteopathic Medicine Nova Southeastern University Fort Lauderdale, FL United States
- Pritzker School of Medicine University of Chicago Chicago, IL United States
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21
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Hurvitz N, Ilan Y. The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems. Clin Pract 2023; 13:994-1014. [PMID: 37623270 PMCID: PMC10453547 DOI: 10.3390/clinpract13040089] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.
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Affiliation(s)
| | - Yaron Ilan
- Hadassah Medical Center, Department of Medicine, Faculty of Medicine, Hebrew University, POB 1200, Jerusalem IL91120, Israel;
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22
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Choi S, Kim S. Artificial Intelligence in the Pathology of Gastric Cancer. J Gastric Cancer 2023; 23:410-427. [PMID: 37553129 PMCID: PMC10412971 DOI: 10.5230/jgc.2023.23.e25] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/09/2023] [Accepted: 07/14/2023] [Indexed: 08/10/2023] Open
Abstract
Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.
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Affiliation(s)
- Sangjoon Choi
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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23
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Pierre K, Gupta M, Raviprasad A, Sadat Razavi SM, Patel A, Peters K, Hochhegger B, Mancuso A, Forghani R. Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges. Expert Rev Anticancer Ther 2023; 23:1265-1279. [PMID: 38032181 DOI: 10.1080/14737140.2023.2286001] [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: 09/01/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential. AREAS COVERED The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential. EXPERT OPINION There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.
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Affiliation(s)
- Kevin Pierre
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Manas Gupta
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
| | - Abheek Raviprasad
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Seyedeh Mehrsa Sadat Razavi
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Anjali Patel
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- University of Florida College of Medicine, Gainesville, FL, USA
| | - Keith Peters
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Bruno Hochhegger
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Anthony Mancuso
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
| | - Reza Forghani
- Radiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Radiology, University of Florida College of Medicine, Gainesville, FL, USA
- Division of Medical Physics, University of Florida College of Medicine, Gainesville, FL, USA
- Department of Neurology, Division of Movement Disorders, University of Florida College of Medicine, Gainesville, FL, USA
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24
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Stashko C, Hayward MK, Northey JJ, Pearson N, Ironside AJ, Lakins JN, Oria R, Goyette MA, Mayo L, Russnes HG, Hwang ES, Kutys ML, Polyak K, Weaver VM. A convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer. Nat Commun 2023; 14:3561. [PMID: 37322009 PMCID: PMC10272194 DOI: 10.1038/s41467-023-39085-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 05/26/2023] [Indexed: 06/17/2023] Open
Abstract
Intratumor heterogeneity associates with poor patient outcome. Stromal stiffening also accompanies cancer. Whether cancers demonstrate stiffness heterogeneity, and if this is linked to tumor cell heterogeneity remains unclear. We developed a method to measure the stiffness heterogeneity in human breast tumors that quantifies the stromal stiffness each cell experiences and permits visual registration with biomarkers of tumor progression. We present Spatially Transformed Inferential Force Map (STIFMap) which exploits computer vision to precisely automate atomic force microscopy (AFM) indentation combined with a trained convolutional neural network to predict stromal elasticity with micron-resolution using collagen morphological features and ground truth AFM data. We registered high-elasticity regions within human breast tumors colocalizing with markers of mechanical activation and an epithelial-to-mesenchymal transition (EMT). The findings highlight the utility of STIFMap to assess mechanical heterogeneity of human tumors across length scales from single cells to whole tissues and implicates stromal stiffness in tumor cell heterogeneity.
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Affiliation(s)
- Connor Stashko
- Department of Surgery, University of California, San Francisco, CA, USA
- Center for Bioengineering and Tissue Regeneration, University of California, San Francisco, San Francisco, CA, USA
| | - Mary-Kate Hayward
- Department of Surgery, University of California, San Francisco, CA, USA
- Center for Bioengineering and Tissue Regeneration, University of California, San Francisco, San Francisco, CA, USA
| | - Jason J Northey
- Department of Surgery, University of California, San Francisco, CA, USA
- Center for Bioengineering and Tissue Regeneration, University of California, San Francisco, San Francisco, CA, USA
| | | | - Alastair J Ironside
- Department of Pathology, Western General Hospital, NHS Lothian, Edinburgh, UK
| | - Johnathon N Lakins
- Department of Surgery, University of California, San Francisco, CA, USA
- Center for Bioengineering and Tissue Regeneration, University of California, San Francisco, San Francisco, CA, USA
| | - Roger Oria
- Department of Surgery, University of California, San Francisco, CA, USA
- Center for Bioengineering and Tissue Regeneration, University of California, San Francisco, San Francisco, CA, USA
| | - Marie-Anne Goyette
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Lakyn Mayo
- Department of Cell and Tissue Biology, School of Dentistry, University of California, San Francisco, San Francisco, CA, USA
| | - Hege G Russnes
- Department of Pathology and Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Institute for Clinical Medicine, University of Oslo, Oslo, Norway
| | - E Shelley Hwang
- Department of Surgery, Duke University Medical Center, Durham, NC, USA
| | - Matthew L Kutys
- Department of Cell and Tissue Biology, School of Dentistry, University of California, San Francisco, San Francisco, CA, USA
- UCSF Helen Diller Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA
| | - Kornelia Polyak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Valerie M Weaver
- Department of Surgery, University of California, San Francisco, CA, USA.
- Center for Bioengineering and Tissue Regeneration, University of California, San Francisco, San Francisco, CA, USA.
- UCSF Helen Diller Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
- Department of Radiation Oncology, Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA.
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25
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Kamulegeya L, Bwanika J, Okello M, Rusoke D, Nassiwa F, Lubega W, Musinguzi D, Börve A. Using artificial intelligence on dermatology conditions in Uganda: a case for diversity in training data sets for machine learning. Afr Health Sci 2023; 23:753-763. [PMID: 38223594 PMCID: PMC10782289 DOI: 10.4314/ahs.v23i2.86] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
Abstract
Background In pursuit of applying universal non-biased Artificial Intelligence (AI) in healthcare, it is essential that data from different geographies are represented. Objective To assess the diagnostic performance of an AI-powered dermatological algorithm called Skin Image Search on Fitzpatrick 6 skin type (dark skin) dermatological conditions. Methods 123 dermatological images selected from a total of 173 images were retrospectively extracted from the electronic database of a Ugandan telehealth company, The Medical Concierge Group (TMCG) after getting their consent. Details of age, gender, and dermatological clinical diagnosis were analysed using R on R studio software to assess the diagnostic accuracy of the AI app along with disease diagnosis and body part. Predictability levels of the AI app were graded on a scale of 0 to 5, where 0- no prediction was made and 1-5 demonstrated a reduction incorrect diagnosis prediction rate of the AI. Results 76 (62%) of the dermatological images were from females and 47 (38%) from males. Overall diagnostic accuracy of the AI app on black dermatological conditions was low at 17% (21 out of 123 predictable images) compared to 69.9% performance on Caucasian skin type as reported from the training results. There were varying predictability levels correctness i.e., 1-8.9%, 2-2.4%, 3-2.4%, 4-1.6%, 5-1.6% with performance along individual diagnosis highest with dermatitis (80%). Conclusion There is need for diversity of image datasets used to train dermatology algorithms for AI applications to increase accuracy across skin types and geographies.
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Affiliation(s)
| | - John Bwanika
- The Medical Concierge Group, Research and Projects
| | - Mark Okello
- The Medical Concierge Group, Information and Technology
| | - Davis Rusoke
- The Medical Concierge Group, Research and Projects
| | - Faith Nassiwa
- The Medical Concierge Group, Information and Technology
| | | | | | - Alexander Börve
- Sahlgrenska University Hospital, Departments of Orthopaedics
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Scholler J, Mandache D, Mathieu MC, Lakhdar AB, Darche M, Monfort T, Boccara C, Olivo-Marin JC, Grieve K, Meas-Yedid V, la Guillaume EBA, Thouvenin O. Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning. J Med Imaging (Bellingham) 2023; 10:034504. [PMID: 37274760 PMCID: PMC10234284 DOI: 10.1117/1.jmi.10.3.034504] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/29/2023] [Accepted: 05/09/2023] [Indexed: 06/06/2023] Open
Abstract
Purpose The adoption of emerging imaging technologies in the medical community is often hampered when they provide a new unfamiliar contrast that requires experience to be interpreted. Dynamic full-field optical coherence tomography (D-FF-OCT) microscopy is such an emerging technique. It provides fast, high-resolution images of excised tissues with a contrast comparable to H&E histology but without any tissue preparation and alteration. Approach We designed and compared two machine learning approaches to support interpretation of D-FF-OCT images of breast surgical specimens and thus provide tools to facilitate medical adoption. We conducted a pilot study on 51 breast lumpectomy and mastectomy surgical specimens and more than 1000 individual 1.3 × 1.3 mm 2 images and compared with standard H&E histology diagnosis. Results Using our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level (1.3 × 1.3 mm 2 ) and above 96% at the specimen level (above cm 2 ). Conclusions Altogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis with minimal sample alteration.
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Affiliation(s)
- Jules Scholler
- PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France
| | - Diana Mandache
- AQUYRE Bioscences-LLTech SAS, Paris, France
- Institut Pasteur, Bioimage Analysis Unit, Paris, France
| | - Marie Christine Mathieu
- Gustave Roussy Cancer Campus, Department of Medical Biology and Pathology, Villejuif, France
| | | | - Marie Darche
- Sorbonne Université, Institut de la Vision, INSERM, CNRS, Paris, France
| | - Tual Monfort
- PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France
| | - Claude Boccara
- PSL University, Institut Langevin, ESPCI Paris, CNRS, Paris, France
| | | | - Kate Grieve
- Sorbonne Université, Institut de la Vision, INSERM, CNRS, Paris, France
- Quinze-Vingts National Eye Hospital, Paris, France
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Ding H, Wu J, Zhao W, Matinlinna JP, Burrow MF, Tsoi JKH. Artificial intelligence in dentistry-A review. FRONTIERS IN DENTAL MEDICINE 2023; 4:1085251. [PMID: 39935549 PMCID: PMC11811754 DOI: 10.3389/fdmed.2023.1085251] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/31/2023] [Indexed: 02/13/2025] Open
Abstract
Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it did not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology-big data (coming through digital devices), computational power, and AI algorithm-in the past two decades, AI applications have started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry are for diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD) is regarded as the gold standard for decision making by dental professionals, while AI machine learning (ML) models learn from human expertise. ML can be seen as another valuable tool to assist dental professionals in multiple stages of clinical cases. This review describes the history and classification of AI, summarizes AI applications in dentistry, discusses the relationship between EBD and ML, and aims to help dental professionals better understand AI as a tool to support their routine work with improved efficiency.
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Affiliation(s)
- Hao Ding
- Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Jiamin Wu
- Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Wuyuan Zhao
- Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Jukka P. Matinlinna
- Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Division of Dentistry, School of Medical Sciences, The University of Manchester, Manchester, United Kingdom
| | - Michael F. Burrow
- Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - James K. H. Tsoi
- Applied Oral Sciences & Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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Kim HJ, Baek EB, Hwang JH, Lim M, Jung WH, Bae MA, Son HY, Cho JW. Application of convolutional neural network for analyzing hepatic fibrosis in mice. J Toxicol Pathol 2023; 36:21-30. [PMID: 36683726 PMCID: PMC9837472 DOI: 10.1293/tox.2022-0066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 09/07/2022] [Indexed: 11/06/2022] Open
Abstract
Recently, with the development of computer vision using artificial intelligence (AI), clinical research on diagnosis and prediction using medical image data has increased. In this study, we applied AI methods to analyze hepatic fibrosis in mice to determine whether an AI algorithm can be used to analyze lesions. Whole slide image (WSI) Sirius Red staining was used to examine hepatic fibrosis. The Xception network, an AI algorithm, was used to train normal and fibrotic lesion identification. We compared the results from two analyses, that is, pathologists' grades and researchers' annotations, to observe whether the automated algorithm can support toxicological pathologists efficiently as a new apparatus. The accuracies of the trained model computed from the training and validation datasets were greater than 99%, and that obtained by testing the model was 100%. In the comparison between analyses, all analyses showed significant differences in the results for each group. Furthermore, both normalized fibrosis grades inferred from the trained model annotated the fibrosis area, and the grades assigned by the pathologists showed significant correlations. Notably, the deep learning algorithm derived the highest correlation with the pathologists' average grade. Owing to the correlation outcomes, we conclude that the trained model might produce results comparable to those of the pathologists' grading of the Sirius Red-stained WSI fibrosis. This study illustrates that the deep learning algorithm can potentially be used for analyzing fibrotic lesions in combination with Sirius Red-stained WSIs as a second opinion tool in non-clinical research.
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Affiliation(s)
- Hyun-Ji Kim
- Toxicological Pathology Research Group, Department of
Advanced Toxicology Research, Korea Institute of Toxicology, 141 Gajeong-ro, Yuseong-gu,
Daejeon 34114, Republic of Korea,College of Veterinary Medicine, Chungnam National
University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea,†These authors contributed equally to this work
| | - Eun Bok Baek
- College of Veterinary Medicine, Chungnam National
University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea
| | - Ji-Hee Hwang
- Toxicological Pathology Research Group, Department of
Advanced Toxicology Research, Korea Institute of Toxicology, 141 Gajeong-ro, Yuseong-gu,
Daejeon 34114, Republic of Korea
| | - Minyoung Lim
- Toxicological Pathology Research Group, Department of
Advanced Toxicology Research, Korea Institute of Toxicology, 141 Gajeong-ro, Yuseong-gu,
Daejeon 34114, Republic of Korea
| | - Won Hoon Jung
- Therapeutics & Biotechnology Division, Korea Research
Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of
Korea
| | - Myung Ae Bae
- Therapeutics & Biotechnology Division, Korea Research
Institute of Chemical Technology, 141 Gajeong-ro, Yuseong-gu, Daejeon 34114, Republic of
Korea
| | - Hwa-Young Son
- College of Veterinary Medicine, Chungnam National
University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Republic of Korea,*Corresponding authors: JW Cho (e-mail: ); HY Son (e-mail: )
| | - Jae-Woo Cho
- Toxicological Pathology Research Group, Department of
Advanced Toxicology Research, Korea Institute of Toxicology, 141 Gajeong-ro, Yuseong-gu,
Daejeon 34114, Republic of Korea,*Corresponding authors: JW Cho (e-mail: ); HY Son (e-mail: )
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Sjöblom N, Boyd S, Manninen A, Blom S, Knuuttila A, Färkkilä M, Arola J. Automated image analysis of keratin 7 staining can predict disease outcome in primary sclerosing cholangitis. Hepatol Res 2022; 53:322-333. [PMID: 36495019 DOI: 10.1111/hepr.13867] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 11/12/2022] [Accepted: 12/02/2022] [Indexed: 12/29/2022]
Abstract
BACKGROUND AND AIMS Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that obstructs the bile ducts and causes liver cirrhosis and cholangiocarcinoma. Efficient surrogate markers are required to measure disease progression. The cytokeratin 7 (K7) load in a liver specimen is an independent prognostic indicator that can be measured from digitalized slides using artificial intelligence (AI)-based models. METHODS A K7-AI model 2.0 was built to measure the hepatocellular K7 load area of the parenchyma, portal tracts, and biliary epithelium. K7-stained PSC liver biopsy specimens (n = 295) were analyzed. A compound endpoint (liver transplantation, liver-related death, and cholangiocarcinoma) was applied in Kaplan-Meier survival analysis to measure AUC values and positive likelihood ratios for each histological variable detected by the model. RESULTS The K7-AI model 2.0 was a better prognostic tool than plasma alkaline phosphatase, the fibrosis stage evaluated by Nakanuma classification, or K7 score evaluated by a pathologist based on the AUC values of measured variables. A combination of parameters, such as portal tract volume and area of K7-positive hepatocytes analyzed by the model, produced an AUC of 0.81 for predicting the compound endpoint. Portal tract volume measured by the model correlated with the histological fibrosis stage. CONCLUSIONS The K7 staining of histological liver specimens in PSC provides significant information on disease outcomes through objective and reproducible data, including variables that cannot be measured by a human pathologist. The K7-AI model 2.0 could serve as a prognostic tool for clinical endpoints and as a surrogate marker in drug trials.
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Affiliation(s)
- Nelli Sjöblom
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Sonja Boyd
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | | | - Sami Blom
- Aiforia Technologies Oyj, Helsinki, Finland
| | | | - Martti Färkkilä
- Department of Gastroenterology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Johanna Arola
- Department of Pathology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
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30
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Gong Y. Study on Machine Translation Teaching Model Based on Translation Parallel Corpus and Exploitation for Multimedia Asian Information Processing. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3523282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Text in one language can be mechanically translated into another language using machine translation (MT). It is possible to anticipate a sequence of words, generally modeling full sentences using machine translation in a single integrated model. Human language's flexibility makes automatic translation an artificial intelligence (AI) challenge of the highest order. A single model rather than a pipeline of fine-tuned models is now the best way to attain state-of-the-art outcomes in machine translation. For example, words having numerous meanings, phrases that use more than one grammatical structure, and other grammar issues make it difficult for a machine to translate; however, many misinterpretations translate to be a breeze. A teacher's job is to assist pupils in overcoming the emotional and cognitive obstacles that stand in the way of developing effective problem-solving abilities. Students will benefit from developing problem-solving abilities since they will apply what they have learned to new circumstances. MT-AI, machine translation technology, and products have been employed in a wide range of applications, including business travel, tourism, and cross-lingual information retrieval. Text translation and phonetic translation are two types of translations that focus on the content of the source language. It is possible to create self-learning systems by injecting machine learning techniques into existing software and then observing the results of such injection. Computer software can translate a massive volume of text in a short period. It takes longer for a human translator to perform the same work as a computer program. The simulation investigation is developed based on correctness and effectiveness, demonstrating the proposed framework's reliability of 95.1%.
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Affiliation(s)
- Yan Gong
- School of Translation and Interpretation of Jilin International Studies University, Changchun 130117, Jilin, China
- College of Humanities, Jilin University, Changchun 130000, Jilin, China
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Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry. Appl Immunohistochem Mol Morphol 2022; 30:668-673. [PMID: 36251973 DOI: 10.1097/pai.0000000000001079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 09/17/2022] [Indexed: 11/25/2022]
Abstract
Invasive breast carcinomas are routinely tested for HER2 using immunohistochemistry (IHC), with reflex in situ hybridization (ISH) for those scored as equivocal (2+). ISH testing is expensive, time-consuming, and not universally available. In this study, we trained a deep learning algorithm to directly predict HER2 gene amplification status from HER2 2+ IHC slides. Data included 115 consecutive cases of invasive breast carcinoma scored as 2+ by IHC that had follow-up HER2 ISH testing. An external validation data set was created from 36 HER2 IHC slides prepared at an outside institution. All internal IHC slides were digitized and divided into training (80%), and test (20%) sets with 5-fold cross-validation. Small patches (256×256 pixels) were randomly extracted and used to train convolutional neural networks with EfficientNet B0 architecture using a transfer learning approach. Predictions for slides in the test set were made on individual patches, and these predictions were aggregated to generate an overall prediction for each slide. This resulted in a receiver operating characteristic area under the curve of 0.83 with an overall accuracy of 79% (sensitivity=0.70, specificity=0.82). Analysis of external validation slides resulted in a receiver operating characteristic area under the curve of 0.79 with an overall accuracy of 81% (sensitivity=0.50, specificity=0.82). Although the sensitivity and specificity are not high enough to negate the need for reflexive ISH testing entirely, this approach may be useful for triaging cases more likely to be HER2 positive and initiating treatment planning in centers where HER2 ISH testing is not readily available.
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Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology. JOURNAL OF MOLECULAR PATHOLOGY 2022. [DOI: 10.3390/jmp3030014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.
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ŞAKIR AA, IŞIK AH, ÖZMEN Ö, İPEK V. Analysis and Estimation of Pathological Data and Findings with Deep Learning Methods. MEHMET AKIF ERSOY ÜNIVERSITESI VETERINER FAKÜLTESI DERGISI 2022. [DOI: 10.24880/maeuvfd.1121112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
As in human diseases, rapid diagnosis of animal diseases is of great importance. In order for the disease treatments to be carried out properly, the diagnosis must be of high accuracy, as well as the rapid diagnosis. In this study, the disease types in the data set consisting of the data examined between the years 2000-2020 belonging to the Department of Pathology of the Faculty of Veterinary Medicine of Burdur Mehmet Akif Ersoy University were estimated by using the decision tree classification model and the KNN classification model. Categories such as age, type, city, and gender in the data set were analyzed in graphics. For the estimation and analysis processes to give accurate results, the data set was corrected by going through some pre-processes and the missing data in the data set was completed. It is thought that the results obtained from the estimation and analysis will allow rapid and accurate diagnosis in animal disease diagnoses.
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Ghareeb WM, Draz E, Madbouly K, Hussein AH, Faisal M, Elkashef W, Emile MH, Edelhamre M, Kim SH, Emile SH. Deep Neural Network for the Prediction of KRAS Genotype in Rectal Cancer. J Am Coll Surg 2022; 235:482-493. [PMID: 35972169 DOI: 10.1097/xcs.0000000000000277] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND KRAS mutation can alter the treatment plan after resection of colorectal cancer. Despite its importance, the KRAS status of several patients remains unchecked because of the high cost and limited resources. This study developed a deep neural network (DNN) to predict the KRAS genotype using hematoxylin and eosin (H&E)-stained histopathological images. STUDY DESIGN Three DNNs were created (KRAS_Mob, KRAS_Shuff, and KRAS_Ince) using the structural backbone of the MobileNet, ShuffleNet, and Inception networks, respectively. The Cancer Genome Atlas was screened to extract 49,684 image tiles that were used for deep learning and internal validation. An independent cohort of 43,032 image tiles was used for external validation. The performance was compared with humans, and a virtual cost-saving analysis was done. RESULTS The KRAS_Mob network (area under the receiver operating curve [AUC] 0.8, 95% CI 0.71 to 0.89) was the best-performing model for predicting the KRAS genotype, followed by the KRAS_Shuff (AUC 0.73, 95% CI 0.62 to 0.84) and KRAS_Ince (AUC 0.71, 95% CI 0.6 to 0.82) networks. Combing the KRAS_Mob and KRAS_Shuff networks as a double prediction approach showed improved performance. KRAS_Mob network accuracy surpassed that of two independent pathologists (AUC 0.79 [95% CI 0.64 to 0.93], 0.51 [95% CI 0.34 to 0.69], and 0.51 (95% CI 0.34 to 0.69]; p < 0.001 for all comparisons). CONCLUSION The DNN has the potential to predict the KRAS genotype directly from H&E-stained histopathological slide images. As an algorithmic screening method to prioritize patients for laboratory confirmation, such a model might possibly reduce the number of patients screened, resulting in significant test-related time and economic savings.
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Affiliation(s)
- Waleed M Ghareeb
- From the Gastrointestinal Surgery Unit (Ghareeb, Hussein), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
- Laboratory of Applied Artificial Intelligence in Medical Disciplines (Ghareeb, Draz, Hussein), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
| | - Eman Draz
- Laboratory of Applied Artificial Intelligence in Medical Disciplines (Ghareeb, Draz, Hussein), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
- Department of Surgery, and Department of Human Anatomy and Embryology (Draz), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
- Key Laboratory of Stem Cell Engineering and Regenerative Medicine, Department of Human Anatomy and Histoembryology, Fujian Medical University, 350122, Fujian Province, Fuzhou City, P.R. China (Draz)
| | - Khaled Madbouly
- Colorectal Surgery Unit, Alexandria University, Faculty of Medicine, Alexandria, Egypt (Madbouly)
| | - Ahmed H Hussein
- From the Gastrointestinal Surgery Unit (Ghareeb, Hussein), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
- Laboratory of Applied Artificial Intelligence in Medical Disciplines (Ghareeb, Draz, Hussein), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
| | - Mohammed Faisal
- Surgical Oncology Unit (Faisal), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
- General Surgery Department, Sahlgrenska University Hospital, Gothenburg, Sweden (Faisal)
| | - Wagdi Elkashef
- Department of Pathology, Faculty of Medicine (Elkashef, M Hany Emile), Mansoura University, Mansoura, Egypt
| | - Mona Hany Emile
- Department of Pathology, Faculty of Medicine (Elkashef, M Hany Emile), Mansoura University, Mansoura, Egypt
| | - Marcus Edelhamre
- the Department of Surgery, Helsingborg Hospital, University of Lund, 25187 Helsingborg, Sweden (Edelhamre)
| | - Seon Hahn Kim
- From the Gastrointestinal Surgery Unit (Ghareeb, Hussein), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
- Laboratory of Applied Artificial Intelligence in Medical Disciplines (Ghareeb, Draz, Hussein), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
- Surgical Oncology Unit (Faisal), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
- Department of Surgery, and Department of Human Anatomy and Embryology (Draz), Faculty of Medicine, Suez Canal University Hospitals, Ismaila, Egypt
- Key Laboratory of Stem Cell Engineering and Regenerative Medicine, Department of Human Anatomy and Histoembryology, Fujian Medical University, 350122, Fujian Province, Fuzhou City, P.R. China (Draz)
- Colorectal Surgery Unit, Alexandria University, Faculty of Medicine, Alexandria, Egypt (Madbouly)
- General Surgery Department, Sahlgrenska University Hospital, Gothenburg, Sweden (Faisal)
- Department of Pathology, Faculty of Medicine (Elkashef, M Hany Emile), Mansoura University, Mansoura, Egypt
- Colorectal Surgery Unit, General Surgery Department (S Hany Emile), Mansoura University, Mansoura, Egypt
- the Department of Surgery, Helsingborg Hospital, University of Lund, 25187 Helsingborg, Sweden (Edelhamre)
| | - Sameh Hany Emile
- Colorectal Surgery Unit, General Surgery Department (S Hany Emile), Mansoura University, Mansoura, Egypt
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Basu S, Agarwal R, Srivastava V. Deep discriminative learning model with calibrated attention map for the automated diagnosis of diffuse large B-cell lymphoma. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Meyer J, Khademi A, Têtu B, Han W, Nippak P, Remisch D. Impact of artificial intelligence on pathologists' decisions: an experiment. J Am Med Inform Assoc 2022; 29:1688-1695. [PMID: 35751441 DOI: 10.1093/jamia/ocac103] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 05/30/2022] [Accepted: 06/09/2022] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVE The accuracy of artificial intelligence (AI) in medicine and in pathology in particular has made major progress but little is known on how much these algorithms will influence pathologists' decisions in practice. The objective of this paper is to determine the reliance of pathologists on AI and to investigate whether providing information on AI impacts this reliance. MATERIALS AND METHODS The experiment using an online survey design. Under 3 conditions, 116 pathologists and pathology students were tasked with assessing the Gleason grade for a series of 12 prostate biopsies: (1) without AI recommendations, (2) with AI recommendations, and (3) with AI recommendations accompanied by information about the algorithm itself, specifically algorithm accuracy rate and algorithm decision-making process. RESULTS Participant responses were significantly more accurate with the AI decision aids than without (92% vs 87%, odds ratio 13.30, P < .01). Unexpectedly, the provision of information on the algorithm made no significant difference compared to AI without information. The reliance on AI correlated with general beliefs on AI's usefulness but not with particular assessments of the AI tool offered. Decisions were made faster when AI was provided. DISCUSSION These results suggest that pathologists are willing to rely on AI regardless of accuracy or explanations. Generalization beyond the specific tasks and explanations provided will require further studies. CONCLUSION This study suggests that the factors that influence the reliance on AI differ in practice from beliefs expressed by clinicians in surveys. Implementation of AI in prospective settings should take individual behaviors into account.
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Affiliation(s)
- Julien Meyer
- School of Health Services Management, Ted Rogers School of Management, Toronto, Ontario, Canada
| | - April Khademi
- Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada
| | - Bernard Têtu
- Départment de biologie médicale, Université Laval, Québec City, Quebec, Canada
| | - Wencui Han
- Department of Business administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Pria Nippak
- School of Health Services Management, Ted Rogers School of Management, Toronto, Ontario, Canada
| | - David Remisch
- School of Health Services Management, Ted Rogers School of Management, Toronto, Ontario, Canada
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Urtnasan E, Lee JH, Moon B, Lee HY, Lee K, Youk H. Noninvasive Screening Tool for Hyperkalemia Using a Single-Lead Electrocardiogram and Deep Learning: Development and Usability Study. JMIR Med Inform 2022; 10:e34724. [PMID: 35657658 PMCID: PMC9206199 DOI: 10.2196/34724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/21/2022] [Accepted: 04/11/2022] [Indexed: 11/29/2022] Open
Abstract
Background Hyperkalemia monitoring is very important in patients with chronic kidney disease (CKD) in emergency medicine. Currently, blood testing is regarded as the standard way to diagnose hyperkalemia (ie, using serum potassium levels). Therefore, an alternative and noninvasive method is required for real-time monitoring of hyperkalemia in the emergency medicine department. Objective This study aimed to propose a novel method for noninvasive screening of hyperkalemia using a single-lead electrocardiogram (ECG) based on a deep learning model. Methods For this study, 2958 patients with hyperkalemia events from July 2009 to June 2019 were enrolled at 1 regional emergency center, of which 1790 were diagnosed with chronic renal failure before hyperkalemic events. Patients who did not have biochemical electrolyte tests corresponding to the original 12-lead ECG signal were excluded. We used data from 855 patients (555 patients with CKD, and 300 patients without CKD). The 12-lead ECG signal was collected at the time of the hyperkalemic event, prior to the event, and after the event for each patient. All 12-lead ECG signals were matched with an electrolyte test within 2 hours of each ECG to form a data set. We then analyzed the ECG signals with a duration of 2 seconds and a segment composed of 1400 samples. The data set was randomly divided into the training set, validation set, and test set according to the ratio of 6:2:2 percent. The proposed noninvasive screening tool used a deep learning model that can express the complex and cyclic rhythm of cardiac activity. The deep learning model consists of convolutional and pooling layers for noninvasive screening of the serum potassium level from an ECG signal. To extract an optimal single-lead ECG, we evaluated the performances of the proposed deep learning model for each lead including lead I, II, and V1-V6. Results The proposed noninvasive screening tool using a single-lead ECG shows high performances with F1 scores of 100%, 96%, and 95% for the training set, validation set, and test set, respectively. The lead II signal was shown to have the highest performance among the ECG leads. Conclusions We developed a novel method for noninvasive screening of hyperkalemia using a single-lead ECG signal, and it can be used as a helpful tool in emergency medicine.
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Affiliation(s)
- Erdenebayar Urtnasan
- Artificial Intelligence Big Data Medical Center, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.,Bigdata Platform Business Group, Yonsei Wonju Health System, Wonju, Republic of Korea
| | - Jung Hun Lee
- Department of Emergency Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
| | - Byungjin Moon
- Bigdata Platform Business Group, Yonsei Wonju Health System, Wonju, Republic of Korea
| | - Hee Young Lee
- Bigdata Platform Business Group, Yonsei Wonju Health System, Wonju, Republic of Korea.,Department of Emergency Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
| | - Kyuhee Lee
- Artificial Intelligence Big Data Medical Center, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea.,Bigdata Platform Business Group, Yonsei Wonju Health System, Wonju, Republic of Korea
| | - Hyun Youk
- Bigdata Platform Business Group, Yonsei Wonju Health System, Wonju, Republic of Korea.,Center of Regional Trauma, Wonju, Republic of Korea
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Saini G, Joshi S, Garlapati C, Li H, Kong J, Krishnamurthy J, Reid MD, Aneja R. Polyploid giant cancer cell characterization: New frontiers in predicting response to chemotherapy in breast cancer. Semin Cancer Biol 2022; 81:220-231. [PMID: 33766651 PMCID: PMC8672208 DOI: 10.1016/j.semcancer.2021.03.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 03/19/2021] [Accepted: 03/20/2021] [Indexed: 02/07/2023]
Abstract
Although polyploid cells were first described nearly two centuries ago, their ability to proliferate has only recently been demonstrated. It also becomes increasingly evident that a subset of tumor cells, polyploid giant cancer cells (PGCCs), play a critical role in the pathophysiology of breast cancer (BC), among other cancer types. In BC, PGCCs can arise in response to therapy-induced stress. Their progeny possess cancer stem cell (CSC) properties and can repopulate the tumor. By modulating the tumor microenvironment (TME), PGCCs promote BC progression, chemoresistance, metastasis, and relapse and ultimately impact the survival of BC patients. Given their pro- tumorigenic roles, PGCCs have been proposed to possess the ability to predict treatment response and patient prognosis in BC. Traditionally, DNA cytometry has been used to detect PGCCs.. The field will further derive benefit from the development of approaches to accurately detect PGCCs and their progeny using robust PGCC biomarkers. In this review, we present the current state of knowledge about the clinical relevance of PGCCs in BC. We also propose to use an artificial intelligence-assisted image analysis pipeline to identify PGCC and map their interactions with other TME components, thereby facilitating the clinical implementation of PGCCs as biomarkers to predict treatment response and survival outcomes in BC patients. Finally, we summarize efforts to therapeutically target PGCCs to prevent chemoresistance and improve clinical outcomes in patients with BC.
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Affiliation(s)
- Geetanjali Saini
- Department of Biology, Georgia State University, Atlanta, GA, USA
| | - Shriya Joshi
- Department of Biology, Georgia State University, Atlanta, GA, USA
| | | | - Hongxiao Li
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA; Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Jun Kong
- Department of Mathematics and Statistics, Georgia State University, Atlanta, GA, USA; Department of Computer Science, Georgia State University, Atlanta, GA, USA; Department of Computer Science, Emory University, Atlanta, GA, USA
| | | | - Michelle D Reid
- Department of Pathology & Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Ritu Aneja
- Department of Biology, Georgia State University, Atlanta, GA, USA.
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Sharedalal P, Singh A, Shah N, Jain D. Automated abstraction of myocardial perfusion imaging reports using natural language processing. J Nucl Cardiol 2022; 29:1188-1190. [PMID: 33474697 DOI: 10.1007/s12350-020-02507-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 12/10/2020] [Indexed: 02/06/2023]
Affiliation(s)
- Parija Sharedalal
- Department of Cardiovascular Medicine, Westchester Medical Center, 100 Woods Road, Valhalla, NY, 10595, USA
| | - Ajay Singh
- Senior Manager Artificial Intelligence, BioXcel Therapeutics Inc, New Haven, CT, USA
| | - Neal Shah
- Department of Medicine, Westchester Medical Center, Valhalla, NY, USA
| | - Diwakar Jain
- Department of Cardiovascular Medicine, Westchester Medical Center, 100 Woods Road, Valhalla, NY, 10595, USA.
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Funer F. The Deception of Certainty: how Non-Interpretable Machine Learning Outcomes Challenge the Epistemic Authority of Physicians. A deliberative-relational Approach. MEDICINE, HEALTH CARE AND PHILOSOPHY 2022; 25:167-178. [PMID: 35538267 PMCID: PMC9089291 DOI: 10.1007/s11019-022-10076-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/03/2022] [Accepted: 03/03/2022] [Indexed: 02/06/2023]
Abstract
Developments in Machine Learning (ML) have attracted attention in a wide range of healthcare fields to improve medical practice and the benefit of patients. Particularly, this should be achieved by providing more or less automated decision recommendations to the treating physician. However, some hopes placed in ML for healthcare seem to be disappointed, at least in part, by a lack of transparency or traceability. Skepticism exists primarily in the fact that the physician, as the person responsible for diagnosis, therapy, and care, has no or insufficient insight into how such recommendations are reached. The following paper aims to make understandable the specificity of the deliberative model of a physician-patient relationship that has been achieved over decades. By outlining the (social-)epistemic and inherently normative relationship between physicians and patients, I want to show how this relationship might be altered by non-traceable ML recommendations. With respect to some healthcare decisions, such changes in deliberative practice may create normatively far-reaching challenges. Therefore, in the future, a differentiation of decision-making situations in healthcare with respect to the necessary depth of insight into the process of outcome generation seems essential.
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Tayebi RM, Mu Y, Dehkharghanian T, Ross C, Sur M, Foley R, Tizhoosh HR, Campbell CJV. Automated bone marrow cytology using deep learning to generate a histogram of cell types. COMMUNICATIONS MEDICINE 2022; 2:45. [PMID: 35603269 PMCID: PMC9053230 DOI: 10.1038/s43856-022-00107-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 03/23/2022] [Indexed: 02/07/2023] Open
Abstract
Background Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited to experienced reference centers and associated with inter-observer variability. This may lead to a delayed or incorrect diagnosis, leaving an unmet need for innovative supporting technologies. Methods We develop an end-to-end deep learning-based system for automated bone marrow cytology. Starting with a bone marrow aspirate digital whole slide image, our system rapidly and automatically detects suitable regions for cytology, and subsequently identifies and classifies all bone marrow cells in each region. This collective cytomorphological information is captured in a representation called Histogram of Cell Types (HCT) quantifying bone marrow cell class probability distribution and acting as a cytological patient fingerprint. Results Our system achieves high accuracy in region detection (0.97 accuracy and 0.99 ROC AUC), and cell detection and cell classification (0.75 mean average precision, 0.78 average F1-score, Log-average miss rate of 0.31). Conclusions HCT has potential to eventually support more efficient and accurate diagnosis in hematology, supporting AI-enabled computational pathology.
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Affiliation(s)
- Rohollah Moosavi Tayebi
- McMaster University, Hamilton, ON Canada
- Kimia Lab, University of Waterloo, Waterloo, ON Canada
| | - Youqing Mu
- McMaster University, Hamilton, ON Canada
| | | | - Catherine Ross
- McMaster University, Hamilton, ON Canada
- Juravinski Hospital and Cancer Centre, Hamilton, ON Canada
| | - Monalisa Sur
- McMaster University, Hamilton, ON Canada
- Juravinski Hospital and Cancer Centre, Hamilton, ON Canada
| | - Ronan Foley
- McMaster University, Hamilton, ON Canada
- Juravinski Hospital and Cancer Centre, Hamilton, ON Canada
| | - Hamid R. Tizhoosh
- Kimia Lab, University of Waterloo, Waterloo, ON Canada
- Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN USA
| | - Clinton J. V. Campbell
- McMaster University, Hamilton, ON Canada
- Juravinski Hospital and Cancer Centre, Hamilton, ON Canada
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Fujii S, Kotani D, Hattori M, Nishihara M, Shikanai T, Hashimoto J, Hama Y, Nishino T, Suzuki M, Yoshidumi A, Ueno M, Komatsu Y, Masuishi T, Hara H, Esaki T, Nakamura Y, Bando H, Yamada T, Yoshino T. Rapid screening using pathomorphological interpretation to detect BRAFV600E mutation and microsatellite instability in colorectal cancer. Clin Cancer Res 2022; 28:2623-2632. [PMID: 35363302 DOI: 10.1158/1078-0432.ccr-21-4391] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 02/18/2022] [Accepted: 03/29/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE Rapid decision-making is essential in precision medicine for initiating molecular targeted therapy for cancer patients. This study aimed to extract pathomorphological features that enable the accurate prediction of genetic abnormalities in cancer from hematoxylin and eosin (H&E) images using deep learning (DL). EXPERIMENTAL DESIGN A total of 1,657 images (one representative image per patient) of thin formalin-fixed, paraffin-embedded (FFPE) tissue sections from either primary or metastatic tumors with next-generation sequencing (NGS)-confirmed genetic abnormalities-including BRAFV600E and KRAS mutations, and microsatellite instability high (MSI-H)-that are directly relevant to therapeutic strategies for advanced colorectal cancer (CRC) were obtained from the nationwide SCRUM-Japan GI-SCREEN project. The images were divided into three groups of 986, 248, and 423 images to create one training and two validation cohorts, respectively. Pathomorphological feature-prediction DL models were first developed based on pathomorphological features. Subsequently, gene-prediction DL models were constructed for all possible combinations of pathomorphological features that enabled the predicting of gene abnormalities based on images filtered by the combination of pathomorphological feature-prediction models. RESULTS High accuracies were achieved, with areas under the curve (AUCs) > 0.90 and 0.80 for 12 and 27, respectively, of 33 analyzed pathomorphological features, with high AUCs being yielded for both BRAFV600E (0.851 and 0.859) and MSI-H (0.923 and 0.862). CONCLUSIONS These findings show that novel next-generation pathology methods can predict genetic abnormalities without the need for standard-of-care gene tests and this novel next-generation pathology method can be applied for CRC treatment planning in the near future.
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Affiliation(s)
| | - Daisuke Kotani
- National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | | | | | | | | | | | | | | | | | - Makoto Ueno
- Kanagawa Cancer Center, Yokohama, Kanagawa, Japan
| | | | | | | | - Taito Esaki
- National Hospital Organization Kyushu Cancer Center, Japan
| | | | - Hideaki Bando
- National Cancer Center Hospital East, Kashiwa, Japan
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Ginghina O, Hudita A, Zamfir M, Spanu A, Mardare M, Bondoc I, Buburuzan L, Georgescu SE, Costache M, Negrei C, Nitipir C, Galateanu B. Liquid Biopsy and Artificial Intelligence as Tools to Detect Signatures of Colorectal Malignancies: A Modern Approach in Patient's Stratification. Front Oncol 2022; 12:856575. [PMID: 35356214 PMCID: PMC8959149 DOI: 10.3389/fonc.2022.856575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/16/2022] [Indexed: 01/19/2023] Open
Abstract
Colorectal cancer (CRC) is the second most frequently diagnosed type of cancer and a major worldwide public health concern. Despite the global efforts in the development of modern therapeutic strategies, CRC prognosis is strongly correlated with the stage of the disease at diagnosis. Early detection of CRC has a huge impact in decreasing mortality while pre-lesion detection significantly reduces the incidence of the pathology. Even though the management of CRC patients is based on robust diagnostic methods such as serum tumor markers analysis, colonoscopy, histopathological analysis of tumor tissue, and imaging methods (computer tomography or magnetic resonance), these strategies still have many limitations and do not fully satisfy clinical needs due to their lack of sensitivity and/or specificity. Therefore, improvements of the current practice would substantially impact the management of CRC patients. In this view, liquid biopsy is a promising approach that could help clinicians screen for disease, stratify patients to the best treatment, and monitor treatment response and resistance mechanisms in the tumor in a regular and minimally invasive manner. Liquid biopsies allow the detection and analysis of different tumor-derived circulating markers such as cell-free nucleic acids (cfNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) in the bloodstream. The major advantage of this approach is its ability to trace and monitor the molecular profile of the patient's tumor and to predict personalized treatment in real-time. On the other hand, the prospective use of artificial intelligence (AI) in medicine holds great promise in oncology, for the diagnosis, treatment, and prognosis prediction of disease. AI has two main branches in the medical field: (i) a virtual branch that includes medical imaging, clinical assisted diagnosis, and treatment, as well as drug research, and (ii) a physical branch that includes surgical robots. This review summarizes findings relevant to liquid biopsy and AI in CRC for better management and stratification of CRC patients.
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Affiliation(s)
- Octav Ginghina
- Department II, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Ariana Hudita
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Marius Zamfir
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Andrada Spanu
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Mara Mardare
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | - Irina Bondoc
- Department of Surgery, “Sf. Ioan” Clinical Emergency Hospital, Bucharest, Romania
| | | | - Sergiu Emil Georgescu
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Marieta Costache
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
| | - Carolina Negrei
- Department of Toxicology, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
| | - Cornelia Nitipir
- Department II, University of Medicine and Pharmacy “Carol Davila” Bucharest, Bucharest, Romania
- Department of Oncology, Elias University Emergency Hospital, Bucharest, Romania
| | - Bianca Galateanu
- Department of Biochemistry and Molecular Biology, University of Bucharest, Bucharest, Romania
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A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif Intell Rev 2022. [DOI: 10.1007/s10462-021-10121-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Bisdas S, Topriceanu CC, Zakrzewska Z, Irimia AV, Shakallis L, Subhash J, Casapu MM, Leon-Rojas J, Pinto dos Santos D, Andrews DM, Zeicu C, Bouhuwaish AM, Lestari AN, Abu-Ismail L, Sadiq AS, Khamees A, Mohammed KMG, Williams E, Omran AI, Ismail DYA, Ebrahim EH. Artificial Intelligence in Medicine: A Multinational Multi-Center Survey on the Medical and Dental Students' Perception. Front Public Health 2021; 9:795284. [PMID: 35004598 PMCID: PMC8739771 DOI: 10.3389/fpubh.2021.795284] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022] Open
Abstract
Background: The emerging field of artificial intelligence (AI) will probably affect the practice for the next generation of doctors. However, the students' views on AI have not been largely investigated. Methods: An anonymous electronic survey on AI was designed for medical and dental students to explore: (1) sources of information about AI, (2) AI applications and concerns, (3) AI status as a topic in medicine, and (4) students' feelings and attitudes. The questionnaire was advertised on social media platforms in 2020. Security measures were employed to prevent fraudulent responses. Mann-Whitney U-test was employed for all comparisons. A sensitivity analysis was also performed by binarizing responses to express disagreement and agreement using the Chi-squared test. Results: Three thousand one hundred thirty-three respondents from 63 countries from all continents were included. Most respondents reported having at least a moderate understanding of the technologies underpinning AI and of their current application, with higher agreement associated with being male (p < 0.0001), tech-savvy (p < 0.0001), pre-clinical student (p < 0.006), and from a developed country (p < 0.04). Students perceive AI as a partner rather than a competitor (72.2%) with a higher agreement for medical students (p = 0.002). The belief that AI will revolutionize medicine and dentistry (83.9%) with greater agreement for students from a developed country (p = 0.0004) was noted. Most students agree that the AI developments will make medicine and dentistry more exciting (69.9%), that AI shall be part of the medical training (85.6%) and they are eager to incorporate AI in their future practice (99%). Conclusion: Currently, AI is a hot topic in medicine and dentistry. Students have a basic understanding of AI principles, a positive attitude toward AI and would like to have it incorporated into their training.
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Affiliation(s)
- Sotirios Bisdas
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, United Kingdom
- Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | | | - Zosia Zakrzewska
- University College London Medical School, University College London, London, United Kingdom
| | | | - Loizos Shakallis
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, United Kingdom
| | - Jithu Subhash
- School of Medicine, Nottingham University, Nottingham, United Kingdom
| | - Maria-Madalina Casapu
- Faculty of Dental Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Jose Leon-Rojas
- NeurALL Research Group, School of Medicine, Ecuador Universidad Internacional del Ecuador, International University of Ecuador, Quito, Ecuador
| | | | | | - Claudia Zeicu
- Department of Clinical Neurophysiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, United Kingdom
| | | | | | | | | | | | | | - Estelle Williams
- Peninsula Dental School, University of Plymouth, Plymouth, United Kingdom
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Xie X, Wang X, Liang Y, Yang J, Wu Y, Li L, Sun X, Bing P, He B, Tian G, Shi X. Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review. Front Oncol 2021; 11:763527. [PMID: 34900711 PMCID: PMC8660076 DOI: 10.3389/fonc.2021.763527] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/18/2021] [Indexed: 12/12/2022] Open
Abstract
Many diseases are accompanied by changes in certain biochemical indicators called biomarkers in cells or tissues. A variety of biomarkers, including proteins, nucleic acids, antibodies, and peptides, have been identified. Tumor biomarkers have been widely used in cancer risk assessment, early screening, diagnosis, prognosis, treatment, and progression monitoring. For example, the number of circulating tumor cell (CTC) is a prognostic indicator of breast cancer overall survival, and tumor mutation burden (TMB) can be used to predict the efficacy of immune checkpoint inhibitors. Currently, clinical methods such as polymerase chain reaction (PCR) and next generation sequencing (NGS) are mainly adopted to evaluate these biomarkers, which are time-consuming and expansive. Pathological image analysis is an essential tool in medical research, disease diagnosis and treatment, functioning by extracting important physiological and pathological information or knowledge from medical images. Recently, deep learning-based analysis on pathological images and morphology to predict tumor biomarkers has attracted great attention from both medical image and machine learning communities, as this combination not only reduces the burden on pathologists but also saves high costs and time. Therefore, it is necessary to summarize the current process of processing pathological images and key steps and methods used in each process, including: (1) pre-processing of pathological images, (2) image segmentation, (3) feature extraction, and (4) feature model construction. This will help people choose better and more appropriate medical image processing methods when predicting tumor biomarkers.
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Affiliation(s)
- Xiaoliang Xie
- Department of Colorectal Surgery, General Hospital of Ningxia Medical University, Yinchuan, China.,College of Clinical Medicine, Ningxia Medical University, Yinchuan, China
| | - Xulin Wang
- Department of Oncology Surgery, Central Hospital of Jia Mu Si City, Jia Mu Si, China
| | - Yuebin Liang
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jingya Yang
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, China
| | - Yan Wu
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Li Li
- Beijing Shanghe Jiye Biotech Co., Ltd., Bejing, China
| | - Xin Sun
- Department of Medical Affairs, Central Hospital of Jia Mu Si City, Jia Mu Si, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,IBMC-BGI Center, T`he Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xiaoli Shi
- Geneis Beijing Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
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Dudgeon SN, Wen S, Hanna MG, Gupta R, Amgad M, Sheth M, Marble H, Huang R, Herrmann MD, Szu CH, Tong D, Werness B, Szu E, Larsimont D, Madabhushi A, Hytopoulos E, Chen W, Singh R, Hart SN, Sharma A, Saltz J, Salgado R, Gallas BD. A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study. J Pathol Inform 2021; 12:45. [PMID: 34881099 PMCID: PMC8609287 DOI: 10.4103/jpi.jpi_83_20] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 01/23/2021] [Accepted: 03/16/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose: Validating artificial intelligence algorithms for clinical use in medical images is a challenging endeavor due to a lack of standard reference data (ground truth). This topic typically occupies a small portion of the discussion in research papers since most of the efforts are focused on developing novel algorithms. In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images. We focus on data collection and evaluation of algorithm performance in the context of estimating the density of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer. Methods: We digitized 64 glass slides of hematoxylin- and eosin-stained invasive ductal carcinoma core biopsies prepared at a single clinical site. A collaborating pathologist selected 10 regions of interest (ROIs) per slide for evaluation. We created training materials and workflows to crowdsource pathologist image annotations on two modes: an optical microscope and two digital platforms. The microscope platform allows the same ROIs to be evaluated in both modes. The workflows collect the ROI type, a decision on whether the ROI is appropriate for estimating the density of sTILs, and if appropriate, the sTIL density value for that ROI. Results: In total, 19 pathologists made 1645 ROI evaluations during a data collection event and the following 2 weeks. The pilot study yielded an abundant number of cases with nominal sTIL infiltration. Furthermore, we found that the sTIL densities are correlated within a case, and there is notable pathologist variability. Consequently, we outline plans to improve our ROI and case sampling methods. We also outline statistical methods to account for ROI correlations within a case and pathologist variability when validating an algorithm. Conclusion: We have built workflows for efficient data collection and tested them in a pilot study. As we prepare for pivotal studies, we will investigate methods to use the dataset as an external validation tool for algorithms. We will also consider what it will take for the dataset to be fit for a regulatory purpose: study size, patient population, and pathologist training and qualifications. To this end, we will elicit feedback from the Food and Drug Administration via the Medical Device Development Tool program and from the broader digital pathology and AI community. Ultimately, we intend to share the dataset, statistical methods, and lessons learned.
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Affiliation(s)
- Sarah N Dudgeon
- Division of Imaging Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiologic Health, United States Food and Drug Administration, White Oak, MD, USA
| | - Si Wen
- Division of Imaging Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiologic Health, United States Food and Drug Administration, White Oak, MD, USA
| | | | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Mohamed Amgad
- Department of Pathology, Northwestern University, Chicago, IL, USA
| | - Manasi Sheth
- Division of Biostatistics, Center for Devices and Radiologic Health, United States Food and Drug Administration, White Oak, MD, USA
| | - Hetal Marble
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Richard Huang
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Markus D Herrmann
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | | | - Evan Szu
- Arrive Bio, San Francisco, CA, USA
| | - Denis Larsimont
- Department of Pathology, Institute Jules Bordet, Brussels, Belgium
| | - Anant Madabhushi
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA
| | | | - Weijie Chen
- Division of Imaging Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiologic Health, United States Food and Drug Administration, White Oak, MD, USA
| | - Rajendra Singh
- Northwell Health and Zucker School of Medicine, New York, NY, USA
| | - Steven N Hart
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Roberto Salgado
- Division of Research, Peter Mac Callum Cancer Centre, Melbourne, Australia.,Department of Pathology, GZA-ZNA Hospitals, Antwerp, Belgium
| | - Brandon D Gallas
- Division of Imaging Diagnostics and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiologic Health, United States Food and Drug Administration, White Oak, MD, USA
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Nawab K, Athwani R, Naeem A, Hamayun M, Wazir M. A Review of Applications of Artificial Intelligence in Gastroenterology. Cureus 2021; 13:e19235. [PMID: 34877212 PMCID: PMC8642128 DOI: 10.7759/cureus.19235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is the science that deals with creating 'intelligent machines'. AI has revolutionized medicine because of its application in several fields across medicine like radiology, neurology, ophthalmology, orthopedics and gastroenterology. In this review, we intend to summarize the basics of AI, the application of AI in various gastrointestinal pathologies till date as well as challenges/ problems related to the application of AI in medicine. Literature search using keywords like artificial intelligence, gastroenterology, applications, etc. were used. The literature search was done using Google Scholar, PubMed and ScienceDirect. All the relevant articles were gathered and relevant data were extracted from them. We concluded AI has achieved major feats in the past few decades. It has helped clinicians in diagnosing complex diseases, managing treatments as well as in predicting outcomes, all in all, which helps doctors from all over the globe in dispensing better healthcare services.
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Affiliation(s)
- Khalid Nawab
- Internal Medicine, Penn State Holy Spirit Hospital, Camp Hill, USA
| | - Ravi Athwani
- Internal Medicine, Penn State Holy Spirit Hospital, Camp Hill, USA
| | - Awais Naeem
- Internal Medicine, Khyber Medical University, Peshawar, PAK
| | | | - Momna Wazir
- Internal Medicine, Hayatabad Medical Complex, Peshawar, PAK
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Großerueschkamp F, Jütte H, Gerwert K, Tannapfel A. Advances in Digital Pathology: From Artificial Intelligence to Label-Free Imaging. Visc Med 2021; 37:482-490. [PMID: 35087898 DOI: 10.1159/000518494] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 07/14/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Digital pathology, in its primary meaning, describes the utilization of computer screens to view scanned histology slides. Digitized tissue sections can be easily shared for a second opinion. In addition, it allows tissue image analysis using specialized software to identify and measure events previously observed by a human observer. These tissue-based readouts were highly reproducible and precise. Digital pathology has developed over the years through new technologies. Currently, the most discussed development is the application of artificial intelligence to automatically analyze tissue images. However, even new label-free imaging technologies are being developed to allow imaging of tissues by means of their molecular composition. SUMMARY This review provides a summary of the current state-of-the-art and future digital pathologies. Developments in the last few years have been presented and discussed. In particular, the review provides an outlook on interesting new technologies (e.g., infrared imaging), which would allow for deeper understanding and analysis of tissue thin sections beyond conventional histopathology. KEY MESSAGES In digital pathology, mathematical methods are used to analyze images and draw conclusions about diseases and their progression. New innovative methods and techniques (e.g., label-free infrared imaging) will bring significant changes in the field in the coming years.
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Affiliation(s)
- Frederik Großerueschkamp
- Center for Protein Diagnostics (PRODI), Biospectroscopy, Ruhr University Bochum, Bochum, Germany.,Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, Bochum, Germany
| | - Hendrik Jütte
- Center for Protein Diagnostics (PRODI), Biospectroscopy, Ruhr University Bochum, Bochum, Germany.,Institute of Pathology, Ruhr University Bochum, Bochum, Germany
| | - Klaus Gerwert
- Center for Protein Diagnostics (PRODI), Biospectroscopy, Ruhr University Bochum, Bochum, Germany.,Department of Biophysics, Faculty of Biology and Biotechnology, Ruhr University Bochum, Bochum, Germany
| | - Andrea Tannapfel
- Center for Protein Diagnostics (PRODI), Biospectroscopy, Ruhr University Bochum, Bochum, Germany.,Institute of Pathology, Ruhr University Bochum, Bochum, Germany
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50
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Kim H, Yoon H, Thakur N, Hwang G, Lee EJ, Kim C, Chong Y. Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain. Sci Rep 2021; 11:22520. [PMID: 34795365 PMCID: PMC8602325 DOI: 10.1038/s41598-021-01905-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 10/28/2021] [Indexed: 02/06/2023] Open
Abstract
Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis.
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Affiliation(s)
- Hyeongsub Kim
- Departments of Electrical Engineering, Creative IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Medical Device Innovation Center, and Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, 37674, South Korea.,Deepnoid Inc., Seoul, 08376, South Korea
| | | | - Nishant Thakur
- Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Uijeongbu St. Mary's Hospital, Seoul, South Korea
| | - Gyoyeon Hwang
- Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Yeouido St. Mary's Hospital, Seoul, South Korea
| | - Eun Jung Lee
- Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Yeouido St. Mary's Hospital, Seoul, South Korea.,Department of Pathology, Shinwon Medical Foundation, Gwangmyeong-si, Gyeonggi-do, South Korea
| | - Chulhong Kim
- Departments of Electrical Engineering, Creative IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Medical Device Innovation Center, and Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, 37674, South Korea.
| | - Yosep Chong
- Department of Hospital Pathology, The Catholic University of Korea, College of Medicine, Uijeongbu St. Mary's Hospital, Seoul, South Korea.
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