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Wang YRJ, Yang K, Wen Y, Wang P, Hu Y, Lai Y, Wang Y, Zhao K, Tang S, Zhang A, Zhan H, Lu M, Chen X, Yang S, Dong Z, Wang Y, Liu H, Zhao L, Huang L, Li Y, Wu L, Chen Z, Luo Y, Liu D, Zhao P, Lin K, Wu JC, Zhao S. Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging. Nat Med 2024; 30:1471-1480. [PMID: 38740996 PMCID: PMC11108784 DOI: 10.1038/s41591-024-02971-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.
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Affiliation(s)
| | - Kai Yang
- Department of Magnetic Resonance Imaging, Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yi Wen
- Changhong AI Research (CHAIR), Sichuan Changhong Electronics Holding Group, Mianyang, China
| | - Pengcheng Wang
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Yuepeng Hu
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Yongfan Lai
- School of Engineering, University of Science and Technology of China, Hefei, China
| | - Yufeng Wang
- Department of Computer Science, Stony Brook University, New York, NY, USA
| | - Kankan Zhao
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Siyi Tang
- School of Medicine, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Angela Zhang
- School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, School of Medicine (Division of Cardiology), Stanford University, Stanford, CA, USA
| | - Huayi Zhan
- Changhong AI Research (CHAIR), Sichuan Changhong Electronics Holding Group, Mianyang, China
| | - Minjie Lu
- Department of Magnetic Resonance Imaging, Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiuyu Chen
- Department of Magnetic Resonance Imaging, Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shujuan Yang
- Department of Magnetic Resonance Imaging, Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhixiang Dong
- Department of Magnetic Resonance Imaging, Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yining Wang
- Peking Union Medical College Hospital, Beijing, China
| | - Hui Liu
- Guangdong Provincial People's Hospital, Guangzhou, China
| | - Lei Zhao
- Beijing Anzhen Hospital, Beijing, China
| | | | - Yunling Li
- The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | | | - Zixian Chen
- The First Hospital of Lanzhou University, Lanzhou, China
| | - Yi Luo
- The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Dongbo Liu
- Changhong AI Research (CHAIR), Sichuan Changhong Electronics Holding Group, Mianyang, China
| | - Pengbo Zhao
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Keldon Lin
- Mayo Clinic Alix School of Medicine, Phoenix, AZ, USA
| | - Joseph C Wu
- School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Cardiovascular Institute, School of Medicine (Division of Cardiology), Stanford University, Stanford, CA, USA
| | - Shihua Zhao
- Department of Magnetic Resonance Imaging, Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Maiter A, Salehi M, Swift AJ, Alabed S. How should studies using AI be reported? lessons from a systematic review in cardiac MRI. FRONTIERS IN RADIOLOGY 2023; 3:1112841. [PMID: 37492379 PMCID: PMC10364997 DOI: 10.3389/fradi.2023.1112841] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/11/2023] [Indexed: 07/27/2023]
Abstract
Recent years have seen a dramatic increase in studies presenting artificial intelligence (AI) tools for cardiac imaging. Amongst these are AI tools that undertake segmentation of structures on cardiac MRI (CMR), an essential step in obtaining clinically relevant functional information. The quality of reporting of these studies carries significant implications for advancement of the field and the translation of AI tools to clinical practice. We recently undertook a systematic review to evaluate the quality of reporting of studies presenting automated approaches to segmentation in cardiac MRI (Alabed et al. 2022 Quality of reporting in AI cardiac MRI segmentation studies-a systematic review and recommendations for future studies. Frontiers in Cardiovascular Medicine 9:956811). 209 studies were assessed for compliance with the Checklist for AI in Medical Imaging (CLAIM), a framework for reporting. We found variable-and sometimes poor-quality of reporting and identified significant and frequently missing information in publications. Compliance with CLAIM was high for descriptions of models (100%, IQR 80%-100%), but lower than expected for descriptions of study design (71%, IQR 63-86%), datasets used in training and testing (63%, IQR 50%-67%) and model performance (60%, IQR 50%-70%). Here, we present a summary of our key findings, aimed at general readers who may not be experts in AI, and use them as a framework to discuss the factors determining quality of reporting, making recommendations for improving the reporting of research in this field. We aim to assist researchers in presenting their work and readers in their appraisal of evidence. Finally, we emphasise the need for close scrutiny of studies presenting AI tools, even in the face of the excitement surrounding AI in cardiac imaging.
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Affiliation(s)
- Ahmed Maiter
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Mahan Salehi
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Andrew J. Swift
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Samer Alabed
- Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- Department of Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
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Nittas V, Daniore P, Landers C, Gille F, Amann J, Hubbs S, Puhan MA, Vayena E, Blasimme A. Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging. PLOS DIGITAL HEALTH 2023; 2:e0000189. [PMID: 36812620 PMCID: PMC9931290 DOI: 10.1371/journal.pdig.0000189] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023]
Abstract
Machine learning has become a key driver of the digital health revolution. That comes with a fair share of high hopes and hype. We conducted a scoping review on machine learning in medical imaging, providing a comprehensive outlook of the field's potential, limitations, and future directions. Most reported strengths and promises included: improved (a) analytic power, (b) efficiency (c) decision making, and (d) equity. Most reported challenges included: (a) structural barriers and imaging heterogeneity, (b) scarcity of well-annotated, representative and interconnected imaging datasets (c) validity and performance limitations, including bias and equity issues, and (d) the still missing clinical integration. The boundaries between strengths and challenges, with cross-cutting ethical and regulatory implications, remain blurred. The literature emphasizes explainability and trustworthiness, with a largely missing discussion about the specific technical and regulatory challenges surrounding these concepts. Future trends are expected to shift towards multi-source models, combining imaging with an array of other data, in a more open access, and explainable manner.
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Affiliation(s)
- Vasileios Nittas
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Paola Daniore
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Constantin Landers
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Felix Gille
- Institute for Implementation Science in Health Care, Faculty of Medicine, University of Zurich, Switzerland
- Digital Society Initiative, University of Zurich, Switzerland
| | - Julia Amann
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Shannon Hubbs
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Milo Alan Puhan
- Epidemiology, Biostatistics and Prevention Institute, Faculty of Medicine, Faculty of Science, University of Zurich, Zurich, Switzerland
| | - Effy Vayena
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
| | - Alessandro Blasimme
- Health Ethics and Policy Lab, Department of Health Sciences and Technology, Swiss Federal Institute of Technology (ETH Zurich), Zurich, Switzerland
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Zakariaee SS, Salmanipour H, Naderi N, Kazemi-Arpanahi H, Shanbehzadeh M. Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis. Clin Transl Imaging 2022; 10:663-676. [PMID: 35892066 PMCID: PMC9302953 DOI: 10.1007/s40336-022-00512-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/05/2022] [Indexed: 01/08/2023]
Abstract
Purpose Chest computed tomography (CT) is a high-sensitivity diagnostic tool for depicting interstitial pneumonia and may lay a critical role in the evaluation of the severity and extent of pulmonary involvement. In this study, we aimed to evaluate the association of chest CT severity score (CT-SS) with the mortality of COVID-19 patients using systematic review and meta-analysis. Methods Web of Science, PubMed, Embase, Scopus, and Google Scholar were used to search for primary articles. The meta-analysis was performed using the random-effects model, and odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated as the effect sizes. Results This meta-analysis retrieved a total number of 7106 COVID-19 patients. The pooled estimate for the association of CT-SS with mortality of COVID-19 patients was calculated as 1.244 (95% CI 1.157–1.337). The pooled estimate for the association of CT-SS with an optimal cutoff and mortality of COVID-19 patients was calculated as 7.124 (95% CI 5.307–9.563). There was no publication bias in the results of included studies. Radiologist experiences and study locations were not potential sources of between-study heterogeneity (both P > 0.2). The shapes of Begg’s funnel plots seemed symmetrical for studies evaluating the association of CT-SS with/without the optimal cutoffs and mortality of COVID-19 patients (Begg’s test P = 0.945 and 0.356, respectively). Conclusions The results of this study point to an association between CT-SS and mortality of COVID-19 patients. The odds of mortality for COVID-19 patients could be accurately predicted using an optimal CT-SS cutoff in visual scoring of lung involvement.
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Affiliation(s)
- Seyed Salman Zakariaee
- Department of Medical Physics, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
| | - Hossein Salmanipour
- Department of Radiology, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran
| | - Negar Naderi
- Department of Midwifery, Faculty of Nursing and Midwifery, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, School of Management and Medical Information Sciences, Abadan University of Medical Sciences, Abadan, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
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Alabed S, Maiter A, Salehi M, Mahmood A, Daniel S, Jenkins S, Goodlad M, Sharkey M, Mamalakis M, Rakocevic V, Dwivedi K, Assadi H, Wild JM, Lu H, O’Regan DP, van der Geest RJ, Garg P, Swift AJ. Quality of reporting in AI cardiac MRI segmentation studies - A systematic review and recommendations for future studies. Front Cardiovasc Med 2022; 9:956811. [PMID: 35911553 PMCID: PMC9334661 DOI: 10.3389/fcvm.2022.956811] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/30/2022] [Indexed: 11/29/2022] Open
Abstract
Background There has been a rapid increase in the number of Artificial Intelligence (AI) studies of cardiac MRI (CMR) segmentation aiming to automate image analysis. However, advancement and clinical translation in this field depend on researchers presenting their work in a transparent and reproducible manner. This systematic review aimed to evaluate the quality of reporting in AI studies involving CMR segmentation. Methods MEDLINE and EMBASE were searched for AI CMR segmentation studies in April 2022. Any fully automated AI method for segmentation of cardiac chambers, myocardium or scar on CMR was considered for inclusion. For each study, compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) was assessed. The CLAIM criteria were grouped into study, dataset, model and performance description domains. Results 209 studies published between 2012 and 2022 were included in the analysis. Studies were mainly published in technical journals (58%), with the majority (57%) published since 2019. Studies were from 37 different countries, with most from China (26%), the United States (18%) and the United Kingdom (11%). Short axis CMR images were most frequently used (70%), with the left ventricle the most commonly segmented cardiac structure (49%). Median compliance of studies with CLAIM was 67% (IQR 59-73%). Median compliance was highest for the model description domain (100%, IQR 80-100%) and lower for the study (71%, IQR 63-86%), dataset (63%, IQR 50-67%) and performance (60%, IQR 50-70%) description domains. Conclusion This systematic review highlights important gaps in the literature of CMR studies using AI. We identified key items missing-most strikingly poor description of patients included in the training and validation of AI models and inadequate model failure analysis-that limit the transparency, reproducibility and hence validity of published AI studies. This review may support closer adherence to established frameworks for reporting standards and presents recommendations for improving the quality of reporting in this field. Systematic Review Registration [www.crd.york.ac.uk/prospero/], identifier [CRD42022279214].
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Affiliation(s)
- Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
- INSIGNEO, Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Ahmed Maiter
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Mahan Salehi
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Aqeeb Mahmood
- Medical School, The University of Sheffield, Sheffield, United Kingdom
| | - Sonali Daniel
- Medical School, The University of Sheffield, Sheffield, United Kingdom
| | - Sam Jenkins
- Medical School, The University of Sheffield, Sheffield, United Kingdom
| | - Marcus Goodlad
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Michael Sharkey
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
| | - Michail Mamalakis
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- INSIGNEO, Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Vera Rakocevic
- Medical School, The University of Sheffield, Sheffield, United Kingdom
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom
| | - Hosamadin Assadi
- University of East Anglia, Norwich Medical School, Norwich, United Kingdom
| | - Jim M. Wild
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- INSIGNEO, Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Haiping Lu
- INSIGNEO, Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom
| | - Declan P. O’Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom
| | | | - Pankaj Garg
- University of East Anglia, Norwich Medical School, Norwich, United Kingdom
| | - Andrew J. Swift
- Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom
- INSIGNEO, Institute for in silico Medicine, The University of Sheffield, Sheffield, United Kingdom
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Yu X, Yao X, Wu B, Zhou H, Xia S, Su W, Wu Y, Zheng X. Using deep learning method to identify left ventricular hypertrophy on echocardiography. Int J Cardiovasc Imaging 2022; 38:759-769. [PMID: 34757566 PMCID: PMC11130004 DOI: 10.1007/s10554-021-02461-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 10/25/2021] [Indexed: 01/19/2023]
Abstract
BACKGROUND Left ventricular hypertrophy (LVH) is an independent prognostic factor for cardiovascular events and it can be detected by echocardiography in the early stage. In this study, we aim to develop a semi-automatic diagnostic network based on deep learning algorithms to detect LVH. METHODS We retrospectively collected 1610 transthoracic echocardiograms, included 724 patients [189 hypertensive heart disease (HHD), 218 hypertrophic cardiomyopathy (HCM), and 58 cardiac amyloidosis (CA), along with 259 controls]. The diagnosis of LVH was defined by two experienced clinicians. For the deep learning architecture, we introduced ResNet and U-net++ to complete classification and segmentation tasks respectively. The models were trained and validated independently. Then, we connected the best-performing models to form the final framework and tested its capabilities. RESULTS In terms of individual networks, the view classification model produced AUC = 1.0. The AUC of the LVH detection model was 0.98 (95% CI 0.94-0.99), with corresponding sensitivity and specificity of 94.0% (95% CI 85.3-98.7%) and 91.6% (95% CI 84.6-96.1%) respectively. For etiology identification, the independent model yielded good results with AUC = 0.90 (95% CI 0.82-0.95) for HCM, AUC = 0.94 (95% CI 0.88-0.98) for CA, and AUC = 0.88 (95% CI 0.80-0.93) for HHD. Finally, our final integrated framework automatically classified four conditions (Normal, HCM, CA, and HHD), which achieved an average of AUC 0.91, with an average sensitivity and specificity of 83.7% and 90.0%. CONCLUSION Deep learning architecture has the ability to detect LVH and even distinguish the latent etiology of LVH.
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Affiliation(s)
- Xiang Yu
- Department of Cardiology, The Fourth Affiliated Hospital, School of Medicine, Zhejiang University, N1 Shangcheng Avenue, Yiwu, 322000, China
| | - Xinxia Yao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Zheda Avenue, Hangzhou, 310027, China
| | - Bifeng Wu
- Department of Cardiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Hong Zhou
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Zheda Avenue, Hangzhou, 310027, China.
| | - Shudong Xia
- Department of Cardiology, The Fourth Affiliated Hospital, School of Medicine, Zhejiang University, N1 Shangcheng Avenue, Yiwu, 322000, China.
| | - Wenwen Su
- Department of Cardiology, The Fourth Affiliated Hospital, School of Medicine, Zhejiang University, N1 Shangcheng Avenue, Yiwu, 322000, China
| | - Yuanyuan Wu
- Department of Cardiology, The Fourth Affiliated Hospital, School of Medicine, Zhejiang University, N1 Shangcheng Avenue, Yiwu, 322000, China
| | - Xiaoye Zheng
- Department of Cardiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
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Peng F, Zheng T, Tang X, Liu Q, Sun Z, Feng Z, Zhao H, Gong L. Magnetic Resonance Texture Analysis in Myocardial Infarction. Front Cardiovasc Med 2021; 8:724271. [PMID: 34778395 PMCID: PMC8581163 DOI: 10.3389/fcvm.2021.724271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 09/27/2021] [Indexed: 11/13/2022] Open
Abstract
Texture analysis (TA) is a newly arisen field that can detect the invisible MRI signal changes among image pixels. Myocardial infarction (MI) is cardiomyocyte necrosis caused by myocardial ischemia and hypoxia, becoming the primary cause of death and disability worldwide. In recent years, various TA studies have been performed in patients with MI and show a good clinical application prospect. This review briefly presents the main pathogenesis and pathophysiology of MI, introduces the overview and workflow of TA, and summarizes multiple magnetic resonance TA (MRTA) clinical applications in MI. We also discuss the facing challenges currently for clinical utilization and propose the prospect.
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Affiliation(s)
- Fei Peng
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tian Zheng
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiaoping Tang
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qiao Liu
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zijing Sun
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhaofeng Feng
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Heng Zhao
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Lianggeng Gong
- Department of Medical Imaging Center, Second Affiliated Hospital of Nanchang University, Nanchang, China
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8
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Lang M, Bernier A, Knoppers BM. AI in Cardiovascular Imaging: "Unexplainable" Legal and Ethical Challenges? Can J Cardiol 2021; 38:225-233. [PMID: 34737036 DOI: 10.1016/j.cjca.2021.10.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/28/2021] [Accepted: 10/28/2021] [Indexed: 02/08/2023] Open
Abstract
Nowhere is the influence of artificial intelligence (AI) likely to be more profoundly felt than in healthcare, from patient triage and diagnosis to surgery and follow-up. Over the medium term, these impacts will be more acute in the cardiovascular imaging context, in which AI models are already successfully performing at roughly human levels of accuracy and efficiency in certain applications. Yet, the adoption of unexplainable AI systems for cardiovascular imaging still raises significant legal and ethical challenges. We focus in particular on challenges posed by the unexplainable character of deep learning and other forms of sophisticated AI modelling used for cardiovascular imaging by briefly outlining the systems being developed in this space, describing how they work, and considering how they might generate outputs that are not reviewable by physicians or system programmers. We suggest that an unexplainable tendency presents two specific ethico-legal concerns: (1) difficulty for health regulators and (2) confusion about the assignment of liability for error or fault in the use of AI systems. We suggest that addressing these concerns is critical for ensuring AI's successful implementation in cardiovascular imaging.
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Affiliation(s)
- Michael Lang
- Academic Associate, Centre of Genomics and Policy, McGill University Faculty of Medicine and Health Sciences
| | - Alexander Bernier
- Academic Associate, Centre of Genomics and Policy, McGill University Faculty of Medicine and Health Sciences
| | - Bartha Maria Knoppers
- Full Professor, Canada Research Chair in Law and Medicine and Director of the Centre of Genomics and Policy, McGill University Faculty of Medicine and Health Sciences.
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O'Shea RJ, Sharkey AR, Cook GJR, Goh V. Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis. Eur Radiol 2021; 31:7969-7983. [PMID: 33860829 PMCID: PMC8452579 DOI: 10.1007/s00330-021-07881-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 02/24/2021] [Accepted: 03/12/2021] [Indexed: 11/05/2022]
Abstract
OBJECTIVES To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis. METHODS A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied. RESULTS One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies documented eligibility criteria for their data (50/186, 95% CI 21-34%), 31% reported demographics for their study population (58/186, 95% CI 25-39%) and 49% of studies assessed model performance on test data partitions (91/186, 95% CI 42-57%). Median CLAIM compliance was 0.40 (IQR 0.33-0.49). Compliance correlated positively with publication year (ρ = 0.15, p = .04) and journal H-index (ρ = 0.27, p < .001). Clinical journals demonstrated higher mean compliance than technical journals (0.44 vs. 0.37, p < .001). CONCLUSIONS Our findings highlight opportunities for improved design and reporting of convolutional neural network research for radiological cancer diagnosis. KEY POINTS • Imaging studies applying convolutional neural networks (CNNs) for cancer diagnosis frequently omit key clinical information including eligibility criteria and population demographics. • Fewer than half of imaging studies assessed model performance on explicitly unobserved test data partitions. • Design and reporting standards have improved in CNN research for radiological cancer diagnosis, though many opportunities remain for further progress.
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Affiliation(s)
- Robert J O'Shea
- Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK.
| | - Amy Rose Sharkey
- Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
- Department of Radiology, Guy's & St Thomas' NHS Foundation Trust, London, UK
| | - Gary J R Cook
- Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
- King's College London & Guy's and St. Thomas' PET Centre, London, UK
| | - Vicky Goh
- Cancer Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, 5th floor, Becket House, 1 Lambeth Palace Road, London, SE1 7EU, UK
- Department of Radiology, Guy's & St Thomas' NHS Foundation Trust, London, UK
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Sermesant M, Delingette H, Cochet H, Jaïs P, Ayache N. Applications of artificial intelligence in cardiovascular imaging. Nat Rev Cardiol 2021; 18:600-609. [PMID: 33712806 DOI: 10.1038/s41569-021-00527-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2021] [Indexed: 01/31/2023]
Abstract
Research into artificial intelligence (AI) has made tremendous progress over the past decade. In particular, the AI-powered analysis of images and signals has reached human-level performance in many applications owing to the efficiency of modern machine learning methods, in particular deep learning using convolutional neural networks. Research into the application of AI to medical imaging is now very active, especially in the field of cardiovascular imaging because of the challenges associated with acquiring and analysing images of this dynamic organ. In this Review, we discuss the clinical questions in cardiovascular imaging that AI can be used to address and the principal methodological AI approaches that have been developed to solve the related image analysis problems. Some approaches are purely data-driven and rely mainly on statistical associations, whereas others integrate anatomical and physiological information through additional statistical, geometric and biophysical models of the human heart. In a structured manner, we provide representative examples of each of these approaches, with particular attention to the underlying computational imaging challenges. Finally, we discuss the remaining limitations of AI approaches in cardiovascular imaging (such as generalizability and explainability) and how they can be overcome.
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Affiliation(s)
| | | | - Hubert Cochet
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
| | - Pierre Jaïs
- IHU Liryc, CHU Bordeaux, Université Bordeaux, Inserm 1045, Pessac, France
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Alabed S, Garg P, Johns CS, Alandejani F, Shahin Y, Dwivedi K, Zafar H, Wild JM, Kiely DG, Swift AJ. Cardiac Magnetic Resonance in Pulmonary Hypertension-an Update. CURRENT CARDIOVASCULAR IMAGING REPORTS 2020; 13:30. [PMID: 33184585 PMCID: PMC7648000 DOI: 10.1007/s12410-020-09550-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/15/2020] [Indexed: 12/28/2022]
Abstract
PURPOSE OF REVIEW This article reviews advances over the past 3 years in cardiac magnetic resonance (CMR) imaging in pulmonary hypertension (PH). We aim to bring the reader up-to-date with CMR applications in diagnosis, prognosis, 4D flow, strain analysis, T1 mapping, machine learning and ongoing research. RECENT FINDINGS CMR volumetric and functional metrics are now established as valuable prognostic markers in PH. This imaging modality is increasingly used to assess treatment response and improves risk stratification when incorporated into PH risk scores. Emerging techniques such as myocardial T1 mapping may play a role in the follow-up of selected patients. Myocardial strain may be used as an early marker for right and left ventricular dysfunction and a predictor for mortality. Machine learning has offered a glimpse into future possibilities. Ongoing research of new PH therapies is increasingly using CMR as a clinical endpoint. SUMMARY The last 3 years have seen several large studies establishing CMR as a valuable diagnostic and prognostic tool in patients with PH, with CMR increasingly considered as an endpoint in clinical trials of PH therapies. Machine learning approaches to improve automation and accuracy of CMR metrics and identify imaging features of PH is an area of active research interest with promising clinical utility.
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Affiliation(s)
- Samer Alabed
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Glossop Road, Sheffield, S10 2JF UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Pankaj Garg
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Glossop Road, Sheffield, S10 2JF UK
| | - Christopher S. Johns
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Glossop Road, Sheffield, S10 2JF UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Faisal Alandejani
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Glossop Road, Sheffield, S10 2JF UK
| | - Yousef Shahin
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Glossop Road, Sheffield, S10 2JF UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Krit Dwivedi
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Glossop Road, Sheffield, S10 2JF UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
| | - Hamza Zafar
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Glossop Road, Sheffield, S10 2JF UK
| | - James M Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Glossop Road, Sheffield, S10 2JF UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
| | - David G Kiely
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Glossop Road, Sheffield, S10 2JF UK
- Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield, UK
| | - Andrew J Swift
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Glossop Road, Sheffield, S10 2JF UK
- Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, UK
- INSIGNEO, Institute for In Silico Medicine, University of Sheffield, Sheffield, UK
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