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诊断性人工智能与心脏疾病

Diagnostic AI and Cardiac Diseases.

作者信息

Uzun Ozsahin Dilber, Ozgocmen Cemre, Balcioglu Ozlem, Ozsahin Ilker, Uzun Berna

机构信息

Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates.

Operational Research Center in Healthcare, Near East University, TRNC Mersin 10, 99138 Nicosia, Turkey.

出版信息

Diagnostics (Basel). 2022 Nov 22;12(12):2901. doi: 10.3390/diagnostics12122901.

DOI:10.3390/diagnostics12122901
PMID:36552908
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9776503/
Abstract

(1) Background: The purpose of this study is to review and highlight recent advances in diagnostic uses of artificial intelligence (AI) for cardiac diseases, in order to emphasize expected benefits to both patients and healthcare specialists; (2) Methods: We focused on four key search terms (Cardiac Disease, diagnosis, artificial intelligence, machine learning) across three different databases (Pubmed, European Heart Journal, Science Direct) between 2017-2022 in order to reach relatively more recent developments in the field. Our review was structured in order to clearly differentiate publications according to the disease they aim to diagnose (coronary artery disease, electrophysiological and structural heart diseases); (3) Results: Each study had different levels of success, where declared sensitivity, specificity, precision, accuracy, area under curve and F1 scores were reported for every article reviewed; (4) Conclusions: the number and quality of AI-assisted cardiac disease diagnosis publications will continue to increase through each year. We believe AI-based diagnosis should only be viewed as an additional tool assisting doctors' own judgement, where the end goal is to provide better quality of healthcare and to make getting medical help more affordable and more accessible, for everyone, everywhere.

摘要

(1)背景:本研究的目的是回顾和强调人工智能(AI)在心脏病诊断应用方面的最新进展,以突出对患者和医疗保健专家的预期益处;(2)方法:我们聚焦于2017年至2022年间三个不同数据库(PubMed、《欧洲心脏杂志》、《科学Direct》)中的四个关键搜索词(心脏病、诊断、人工智能、机器学习),以获取该领域相对较新的进展。我们的综述结构旨在根据所针对诊断的疾病(冠状动脉疾病、电生理和结构性心脏病)清晰区分出版物;(3)结果:每项研究的成功程度各不相同,对每篇综述文章均报告了所宣称的敏感性、特异性、精确性、准确性、曲线下面积和F1分数;(4)结论:人工智能辅助心脏病诊断出版物的数量和质量将逐年持续增加。我们认为基于人工智能的诊断应仅被视为辅助医生自身判断的额外工具,最终目标是为各地的每个人提供更高质量的医疗保健,并使获得医疗帮助更加经济实惠且易于实现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6d5/9776503/06e1dc254b26/diagnostics-12-02901-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6d5/9776503/69c1f1a22b7a/diagnostics-12-02901-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6d5/9776503/b74678fd54a5/diagnostics-12-02901-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6d5/9776503/06e1dc254b26/diagnostics-12-02901-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6d5/9776503/69c1f1a22b7a/diagnostics-12-02901-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6d5/9776503/b74678fd54a5/diagnostics-12-02901-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a6d5/9776503/06e1dc254b26/diagnostics-12-02901-g003.jpg

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