Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department.
Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department.
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DOI：
10.1002/emp2.12297
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发表时间：
2020-12
影响因子：
2.3
通讯作者：
Dameff C
中科院分区：
文献类型：
--
作者：
Carlile M;Hurt B;Hsiao A;Hogarth M;Longhurst CA;Dameff C
2019年冠状病毒病大流行激发了在资源有限的高人口普查条件下诊断、治疗和处置患者的新创新。我们的目标是描述医生与一种新型人工智能（AI）算法互动的首次经验，该算法旨在提高医生识别胸片上磨玻璃样阴影和实变的能力。在第一波疫情期间，我们部署了一种先前开发并验证的深度学习人工智能算法，用于辅助解读胸片，供南加州一个学术卫生系统的医生使用。该算法将X光片与“热图”叠加，表明肺炎概率与标准胸部X光片在护理点。对医生进行了关于易用性和对临床决策影响的真实的时间调查。在研究期间，在急诊科（艾德）获得的5125次总访视和1960张胸片中，1855张通过算法进行了分析。其中，急诊医生接受了202张X光片的经验调查。总体而言，86%的人强烈同意或有点同意干预措施在他们的工作流程中易于使用。在受访者中，20%的人表示该算法影响了临床决策。据我们所知，这是第一篇发表的文献，评估了医学成像AI对急诊科临床决策的影响。在临床上紧急部署之前经过验证的人工智能算法易于使用，并被发现在全球大流行的预测激增期间对临床决策产生影响。
The coronavirus disease 2019 pandemic has inspired new innovations in diagnosing, treating, and dispositioning patients during high census conditions with constrained resources. Our objective is to describe first experiences of physician interaction with a novel artificial intelligence (AI) algorithm designed to enhance physician abilities to identify ground‐glass opacities and consolidation on chest radiographs. During the first wave of the pandemic, we deployed a previously developed and validated deep‐learning AI algorithm for assisted interpretation of chest radiographs for use by physicians at an academic health system in Southern California. The algorithm overlays radiographs with “heat” maps that indicate pneumonia probability alongside standard chest radiographs at the point of care. Physicians were surveyed in real time regarding ease of use and impact on clinical decisionmaking. Of the 5125 total visits and 1960 chest radiographs obtained in the emergency department (ED) during the study period, 1855 were analyzed by the algorithm. Among these, emergency physicians were surveyed for their experiences on 202 radiographs. Overall, 86% either strongly agreed or somewhat agreed that the intervention was easy to use in their workflow. Of the respondents, 20% reported that the algorithm impacted clinical decisionmaking. To our knowledge, this is the first published literature evaluating the impact of medical imaging AI on clinical decisionmaking in the emergency department setting. Urgent deployment of a previously validated AI algorithm clinically was easy to use and was found to have an impact on clinical decision making during the predicted surge period of a global pandemic.
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DOI：
10.1016/j.jaip.2020.06.038
发表时间：
2020-09-01
影响因子：
9.4
作者：
Hare, Nathan;Bansal, Priya;Mosnaim, Giselle S.
通讯作者：
Mosnaim, Giselle S.
影响因子：
4.1
作者：
Benson, Tim
通讯作者：
Benson, Tim
DOI：
10.1148/ryct.2020200034
发表时间：
2020-02-01
期刊：
RADIOLOGY-CARDIOTHORACIC IMAGING
影响因子：
--
作者：
Ng, Ming-Yen;Lee, Elaine Y. P.;Kuo, Michael D.
通讯作者：
Kuo, Michael D.
影响因子：
4.5
作者：
Allen, Bibb, Jr.;Seltzer, Steven E.;Kandarpa, Krishna
通讯作者：
Kandarpa, Krishna
影响因子：
4.4
作者：
Chase, Valerie J.;Cohn, Amy E. M.;Lavieri, Mariel S.
通讯作者：
Lavieri, Mariel S.