人工智能支持的心电图:生理和病理生理的见解和意义。

IF 4.2 2区医学 Q1 PHYSIOLOGY

Comprehensive Physiology Pub Date : 2022-06-29 DOI:10.1002/cphy.c210001

Anthony H Kashou, Demilade A Adedinsewo, Konstantinos C Siontis, Peter A Noseworthy

{"title":"人工智能支持的心电图:生理和病理生理的见解和意义。","authors":"Anthony H Kashou, Demilade A Adedinsewo, Konstantinos C Siontis, Peter A Noseworthy","doi":"10.1002/cphy.c210001","DOIUrl":null,"url":null,"abstract":"Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417-3424, 2022.","PeriodicalId":10573,"journal":{"name":"Comprehensive Physiology","volume":"12 3","pages":"3417-3424"},"PeriodicalIF":4.2000,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795459/pdf/nihms-1852005.pdf","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications.\",\"authors\":\"Anthony H Kashou, Demilade A Adedinsewo, Konstantinos C Siontis, Peter A Noseworthy\",\"doi\":\"10.1002/cphy.c210001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417-3424, 2022.\",\"PeriodicalId\":10573,\"journal\":{\"name\":\"Comprehensive Physiology\",\"volume\":\"12 3\",\"pages\":\"3417-3424\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2022-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9795459/pdf/nihms-1852005.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Comprehensive Physiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/cphy.c210001\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Comprehensive Physiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/cphy.c210001","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

机器学习和计算方法的进步给迄今为止最重要的诊断工具之一——心电图(ECG)带来了新的生命和极大的兴奋。人工智能心电图(AI-ECG)的应用已经产生了识别常规和独特变量和病理的心电图特征的能力，为巨大的临床潜力让路。然而，这些人工智能心电图模型检测到人眼缺失的东西仍不清楚。在本文中，我们重点介绍了该领域的一些最新进展及其潜在的临床意义，同时也试图阐明使这些模型具有如此高诊断率的生理和病理生理特征。©2022美国生理学会。中国生物医学工程学报(英文版)，2012。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications.

查看原文本刊更多论文

Artificial Intelligence-Enabled ECG: Physiologic and Pathophysiologic Insights and Implications.

Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417-3424, 2022.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Comprehensive Physiology PHYSIOLOGY-

CiteScore

10.50

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Comprehensive Physiology is the most authoritative and comprehensive collection of physiology information ever assembled, and uses the most powerful features of review journals and electronic reference works to cover the latest key developments in the field, through the most authoritative articles on the subjects covered. This makes Comprehensive Physiology a valued reference work on the evolving science of physiology for both researchers and clinicians. It also provides a useful teaching tool for instructors and an informative resource for medical students and other students in the life and health sciences.