{"title":"人工智能在心脏成像中的应用","authors":"K. Seetharam, S. Shrestha, P. Sengupta","doi":"10.15420/usc.2019.19.2","DOIUrl":null,"url":null,"abstract":"Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.","PeriodicalId":37809,"journal":{"name":"US Cardiology Review","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Artificial Intelligence in Cardiac Imaging\",\"authors\":\"K. Seetharam, S. Shrestha, P. Sengupta\",\"doi\":\"10.15420/usc.2019.19.2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.\",\"PeriodicalId\":37809,\"journal\":{\"name\":\"US Cardiology Review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"US Cardiology Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15420/usc.2019.19.2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"US Cardiology Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15420/usc.2019.19.2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.
期刊介绍：
US Cardiology Review (USC) is an international, US-English language, peer-reviewed journal that is published bi-annually and aims to assist time-pressured physicians to stay abreast of key advances and opinion in the area of cardiovascular disease. The journal comprises balanced and comprehensive review articles written by leading authorities. The journal provides updates on a range of salient issues to support physicians in developing their knowledge and effectiveness in day-to-day clinical practice. The journal endeavours to support the continuous medical education of specialist and general cardiologists and disseminate knowledge of the field to the wider cardiovascular community.
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