{"id":17573,"date":"2019-08-21T05:38:20","date_gmt":"2019-08-20T21:38:20","guid":{"rendered":"http:\/\/csccm.org.cn\/?p=17573"},"modified":"2019-08-28T22:52:01","modified_gmt":"2019-08-28T14:52:01","slug":"lancet%e5%9c%a8%e7%ba%bf%e5%8f%91%e8%a1%a8%ef%bc%9a%e5%bf%83%e7%94%b5%e5%9b%beai%e7%ae%97%e6%b3%95%e9%89%b4%e5%88%ab%e7%aa%a6%e6%80%a7%e5%bf%83%e5%be%8b%e6%97%b6%e7%9a%84%e6%88%bf%e9%a2%a4","status":"publish","type":"post","link":"https:\/\/csccm.org.cn\/?p=17573","title":{"rendered":"[Lancet\u5728\u7ebf\u53d1\u8868]\uff1a\u5fc3\u7535\u56feAI\u7b97\u6cd5\u9274\u522b\u7aa6\u6027\u5fc3\u5f8b\u65f6\u7684\u623f\u98a4"},"content":{"rendered":"\n

ARTICLES|ONLINE FIRST<\/a><\/p>\n\n\n\n

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction<\/h1>\n\n\n\n

Zachi I Attia, Peter A Noseworthy, Francisco Lopez-Jimenez, et al<\/h3>\n\n\n\n

Lancet Published:August 01, 2019 DOI:https:\/\/doi.org\/10.1016\/S0140-6736(19)31721-0<\/a><\/h3>\n\n\n\n

Summary<\/h2>\n\n\n\n

Background \u80cc\u666f<\/h3>\n\n\n\n

Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning.<\/p>\n\n\n\n

\u623f\u98a4\u5e38\u5e38\u6ca1\u6709\u4e34\u5e8a\u75c7\u72b6\uff0c\u56e0\u800c\u96be\u4ee5\u53d1\u73b0\uff0c\u4f46\u4f34\u968f\u5352\u4e2d\u3001\u5fc3\u8870\u548c\u6b7b\u4ea1\u98ce\u9669\u589e\u52a0\u3002\u73b0\u6709\u7684\u7b5b\u67e5\u65b9\u6cd5\u9700\u8981\u901a\u8fc7\u957f\u65f6\u95f4\u76d1\u6d4b\u5b9e\u73b0\uff0c\u53d7\u5230\u8d39\u7528\u8f83\u9ad8\u53ca\u68c0\u51fa\u7387\u4f4e\u7b49\u56e0\u7d20\u7684\u5c40\u9650\u3002\u6211\u4eec\u65e8\u5728\u901a\u8fc7\u673a\u5668\u5b66\u4e60\u5efa\u7acb\u4e00\u79cd\u5feb\u901f\u3001\u4fbf\u5b9c\u7684\u5e8a\u65c1\u76d1\u6d4b\u65b9\u6cd5\uff0c\u9274\u522b\u623f\u98a4\u60a3\u8005\u3002<\/p>\n\n\n\n

Methods \u65b9\u6cd5<\/h3>\n\n\n\n

We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. We included all patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. We classified patients with at least one ECG with a rhythm of atrial fibrillation or atrial flutter as positive for atrial fibrillation. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operatoring characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the accuracy, sensitivity, specificity, and F1 score with two-sided 95% CIs.<\/p>\n\n\n\n

\u6211\u4eec\u91c7\u7528\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u65b9\u6cd5\u5efa\u7acbECG\u7684\u4eba\u5de5\u667a\u80fd(AI)\u7b97\u6cd5\uff0c\u901a\u8fc7\u6807\u51c6\u768410\u79d212\u5bfcECG\uff0c\u5728\u6b63\u5e38\u7aa6\u6027\u5fc3\u5f8b\u65f6\u68c0\u6d4b\u623f\u98a4\u7684ECG\u7279\u5f81\u3002\u6211\u4eec\u7eb3\u51651993\u5e7412\u670831\u65e5\u81f32017\u5e747\u670821\u65e5\u95f4Mayo\u8bca\u6240ECG\u5b9e\u9a8c\u5ba4\u8fdb\u884cECG\u68c0\u67e5\u7684\u6240\u670918\u5c81\u4ee5\u4e0a\u60a3\u8005\u3002\u60a3\u8005\u5728\u4ef0\u5367\u4f4d\u65f6\u63a5\u53d7ECG\u68c0\u67e5\uff0c\u4e14\u81f3\u5c11\u6709\u4e00\u4e2a\u6570\u5b57\u5316\u768410\u79d2\u949f12\u5bfc\u8054\u6807\u51c6ECG\u68c0\u67e5\u7ed3\u679c\u3002\u5728\u5fc3\u810f\u5185\u79d1\u4e13\u5bb6\u76d1\u7763\u4e0b\uff0c\u7ecf\u8fc7\u57f9\u8bad\u7684\u4eba\u5458\u786e\u8ba4ECG\u8282\u5f8b\u3002\u6211\u4eec\u5c06\u81f3\u5c11\u4e00\u4e2aECG\u663e\u793a\u4e3a\u623f\u98a4\u6216\u623f\u6251\u6212\u5f8b\u7684\u60a3\u8005\u5b9a\u4e49\u4e3a\u623f\u98a4\u60a3\u8005\u3002\u6211\u4eec\u6839\u636e7:1:2\u7684\u6bd4\u4f8b\uff0c\u5c06ECG\u5206\u4e3a\u8bad\u7ec3\u6570\u636e\u96c6\u3001\u5185\u90e8\u9a8c\u8bc1\u6570\u636e\u96c6\u548c\u68c0\u9a8c\u6570\u636e\u96c6\u3002\u9488\u5bf9\u5185\u90e8\u9a8c\u8bc1\u6570\u636e\u96c6\uff0c\u6211\u4eec\u8ba1\u7b97ROC\u66f2\u7ebf\u4e0b\u9762\u79ef(AUC)\uff0c\u4ee5\u9009\u62e9\u7528\u4e8e\u68c0\u9a8c\u6570\u636e\u96c6\u7684\u6982\u7387\u9608\u503c\u3002\u6211\u4eec\u6839\u636eAUC\u3001\u51c6\u786e\u6027\u3001\u654f\u611f\u6027\u3001\u7279\u5f02\u6027\u548cF1\u8bc4\u5206\uff08\u53cc\u4fa795%CI\uff09\u8bc4\u4ef7\u68c0\u9a8c\u6570\u636e\u96c6\u6a21\u578b\u7684\u51c6\u786e\u6027\u3002<\/p>\n\n\n\n

Findings \u7ed3\u679c<\/h3>\n\n\n\n

We included 180\u2008922 patients with 649\u2008931 normal sinus rhythm ECGs for analysis: 454\u2008789 ECGs recorded from 126\u2008526 patients in the training dataset, 64\u2008340 ECGs from 18\u2008116 patients in the internal validation dataset, and 130\u2008802 ECGs from 36\u2008280 patients in the testing dataset. 3051 (8\u00b74%) patients in the testing dataset had verified atrial fibrillation before the normal sinus rhythm ECG tested by the model. A single AI-enabled ECG identified atrial fibrillation with an AUC of 0\u00b787 (95% CI 0\u00b786\u20130\u00b788), sensitivity of 79\u00b70% (77\u00b75\u201380\u00b74), specificity of 79\u00b75% (79\u00b70\u201379\u00b79), F1 score of 39\u00b72% (38\u00b71\u201340\u00b73), and overall accuracy of 79\u00b74% (79\u00b70\u201379\u00b79). Including all ECGs acquired during the first month of each patient's window of interest (ie, the study start date or 31 days before the first recorded atrial fibrillation ECG) increased the AUC to 0\u00b790 (0\u00b790\u20130\u00b791), sensitivity to 82\u00b73% (80\u00b79\u201383\u00b76), specificity to 83\u00b74% (83\u00b70\u201383\u00b78), F1 score to 45\u00b74% (44\u00b72\u201346\u00b75), and overall accuracy to 83\u00b73% (83\u00b70\u201383\u00b77).<\/p>\n\n\n\n

\u6211\u4eec\u7eb3\u5165\u4e86180\u2008922 \u540d\u60a3\u8005\u7684 649\u2008931 \u4efd\u6b63\u5e38\u7aa6\u6027\u5fc3\u5f8b ECGs \u7528\u4e8e\u5206\u6790\uff1a\u8bad\u7ec3\u6570\u636e\u96c6\u5305\u62ec126 525 \u540d\u60a3\u8005\u7684 454\u2008789 \u4efd ECG\uff0c\u5185\u90e8\u9a8c\u8bc1\u6570\u636e\u96c6\u5305\u62ec 18 116 \u540d\u60a3\u8005\u7684 64\u2008340 \u4efd ECG\uff0c\u68c0\u9a8c\u6570\u636e\u96c6\u5305\u62ec36 280 \u540d\u60a3\u8005\u7684 130\u2008802 \u4efd ECG\u3002\u5728\u6b63\u5e38\u7aa6\u6027\u5fc3\u5f8bECG\u7528\u4e8e\u6a21\u578b\u68c0\u9a8c\u524d\uff0c\u68c0\u9a8c\u6570\u636e\u96c6\u4e2d 3051 \u540d (8\u00b74%) \u60a3\u8005\u88ab\u786e\u8ba4\u4e3a\u623f\u98a4\u3002\u5355\u6b21AI \u5206\u6790 ECG \u9274\u522b\u623f\u98a4\u7684 AUC 0\u00b787 (95% CI 0\u00b786\u20130\u00b788)\uff0c\u654f\u611f\u6027 79\u00b70% (77\u00b75\u201380\u00b74)\uff0c\u7279\u5f02\u6027 79\u00b75% (79\u00b70\u201379\u00b79)\uff0cF1 \u8bc4\u5206 39\u00b72% (38\u00b71\u201340\u00b73)\uff0c\u603b\u7684\u51c6\u786e\u6027\u4e3a 79\u00b74% (79\u00b70\u201379\u00b79)\u3002\u5bf9\u4e8e\u6bcf\u4f4d\u60a3\u8005\uff0c\u7eb3\u5165\u7814\u7a76\u7a97\u9996\u6708\u5185\u6240\u6709ECG\uff08\u5373\u7814\u7a76\u5f00\u59cb\u65e5\u671f\u6216\u9996\u6b21\u8bb0\u5f55\u623f\u98a4\u524d31\u5929\uff09\uff0c\u53ef\u4f7f AUC \u589e\u52a0\u5230 0\u00b790 (0\u00b790\u20130\u00b791)\uff0c\u654f\u611f\u6027\u589e\u52a0\u5230 82\u00b73% (80\u00b79\u201383\u00b76)\uff0c\u7279\u5f02\u6027 83\u00b74% (83\u00b70\u201383\u00b78), F1 \u8bc4\u5206 45\u00b74% (44\u00b72\u201346\u00b75)\uff0c\u603b\u4f53\u51c6\u786e\u6027 83\u00b73% (83\u00b70\u201383\u00b77)\u3002<\/p>\n\n\n\n

\"\"\/<\/figure>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n

Interpretation \u7ed3\u8bba<\/h3>\n\n\n\n

An AI-enabled ECG acquired during normal sinus rhythm permits identification at point of care of individuals with atrial fibrillation.<\/p>\n\n\n\n

\u91c7\u7528AI\u5206\u6790\u6b63\u5e38\u7aa6\u6027\u5fc3\u5f8b\u4e0b\u7684ECG\uff0c\u80fd\u591f\u5728\u5e8a\u65c1\u9274\u522b\u623f\u98a4\u60a3\u8005\u3002<\/p>\n\n\n\n

Funding \u8d44\u52a9<\/h3>\n\n\n\n

None.<\/p>\n","protected":false},"excerpt":{"rendered":"

ARTICLES|ONLINE FIRST An artificial intelligence-enable […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[32,23],"tags":[],"_links":{"self":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts\/17573"}],"collection":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=17573"}],"version-history":[{"count":3,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts\/17573\/revisions"}],"predecessor-version":[{"id":17588,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=\/wp\/v2\/posts\/17573\/revisions\/17588"}],"wp:attachment":[{"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17573"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17573"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/csccm.org.cn\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17573"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}