{"id":7001,"date":"2020-05-20T15:50:21","date_gmt":"2020-05-20T22:50:21","guid":{"rendered":"https:\/\/depts.washington.edu\/pandemicalliance\/?p=7001"},"modified":"2021-03-19T15:51:14","modified_gmt":"2021-03-19T22:51:14","slug":"artificial-intelligence-enabled-rapid-diagnosis-of-patients-with-covid-19","status":"publish","type":"post","link":"https:\/\/depts.washington.edu\/pandemicalliance\/2020\/05\/20\/artificial-intelligence-enabled-rapid-diagnosis-of-patients-with-covid-19\/","title":{"rendered":"Artificial Intelligence-Enabled Rapid Diagnosis of Patients with COVID-19"},"content":{"rendered":"<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"34\" data-aria-posinset=\"2019\" data-aria-level=\"1\"><span data-contrast=\"auto\">Mei et al.<\/span><span data-contrast=\"auto\">\u00a0developed an\u00a0<\/span><span data-contrast=\"auto\">artificial intelligence (<\/span><span data-contrast=\"auto\">AI<\/span><span data-contrast=\"auto\">)<\/span><span data-contrast=\"auto\">\u00a0algorithm that integrates chest CT findings with clinical history to provide rapid diagnosis of COVID-19<\/span><span data-contrast=\"auto\">. The algorithm<\/span><span data-contrast=\"auto\">\u00a0achieve<\/span><span data-contrast=\"auto\">d<\/span><span data-contrast=\"auto\">\u00a0<\/span><span data-contrast=\"auto\">high discriminative performance (<\/span><span data-contrast=\"auto\">AUC<\/span><span data-contrast=\"auto\">=<\/span><span data-contrast=\"auto\">0.92<\/span><span data-contrast=\"auto\">)\u00a0<\/span><span data-contrast=\"auto\">with equal sensitivity compared to a senior thoracic radiologist (84.3% vs 74.6%) on a test set of 279 patients.<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"34\" data-aria-posinset=\"2019\" data-aria-level=\"1\"><span data-contrast=\"auto\">The algorithm also correctly identified 17 of 25 patients who\u00a0presented\u00a0with normal CT scans but had positive RT-PCR results, whereas radiologists classified all as COVID-19 negative<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"34\" data-aria-posinset=\"2019\" data-aria-level=\"1\"><span data-contrast=\"auto\">While the generalizability of the algorithm is limited\u00a0by\u00a0sample size, it has useful implications as a potential screening tool.<\/span><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><i><span data-contrast=\"none\">Mei et al. (May 19, 2020). Artificial Intelligence-Enabled Rapid Diagnosis of Patients with COVID-19. Nature Medicine.\u00a0<\/span><\/i><a href=\"https:\/\/doi.org\/10.1038\/s41591-020-0931-3\"><span data-contrast=\"none\">https:\/\/doi.org\/10.1038\/s41591-020-0931-3<\/span><\/a><i><span data-contrast=\"none\">\u00a0<\/span><\/i><span data-ccp-props=\"{&quot;134233279&quot;:true,&quot;201341983&quot;:0,&quot;335559685&quot;:720,&quot;335559739&quot;:160,&quot;335559740&quot;:259}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mei et al.\u00a0developed an\u00a0artificial intelligence (AI)\u00a0algorithm that integrates chest CT findings with clinical history to provide rapid diagnosis of COVID-19. The algorithm\u00a0achieved\u00a0high discriminative performance (AUC=0.92)\u00a0with equal sensitivity compared to a senior thoracic radiologist (84.3% vs 74.6%) on a test set of 279 patients.\u00a0 The algorithm also correctly identified 17 of 25 patients who\u00a0presented\u00a0with normal CT&#8230;<\/p>\n<div><a class=\"more\" href=\"https:\/\/depts.washington.edu\/pandemicalliance\/2020\/05\/20\/artificial-intelligence-enabled-rapid-diagnosis-of-patients-with-covid-19\/\">Read more<\/a><\/div>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[6],"tags":[],"topic":[19],"class_list":["post-7001","post","type-post","status-publish","format-standard","hentry","category-article-summary","topic-testing-and-treatment"],"_links":{"self":[{"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts\/7001","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/comments?post=7001"}],"version-history":[{"count":1,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts\/7001\/revisions"}],"predecessor-version":[{"id":7002,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/posts\/7001\/revisions\/7002"}],"wp:attachment":[{"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/media?parent=7001"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/categories?post=7001"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/tags?post=7001"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/depts.washington.edu\/pandemicalliance\/wp-json\/wp\/v2\/topic?post=7001"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}