Artificial intelligence for stepwise diagnosis and monitoring of COVID-19.
Hengrui LiangYuchen GuoXiangru ChenKeng-Leong AngYuwei HeNa JiangQiang DuQingsi ZengLigong LuZebin GaoLinduo LiQuanzheng LiFangxing NieGuiguang DingGao HuangAilan ChenYimin LiWeijie GuanLing SangYuanda XuHuai ChenZisheng ChenShiyue LiNuofu ZhangYing ChenDanxia HuangRun LiJianfu LiBo ChengYi ZhaoCaichen LiShan XiongRunchen WangJun LiuWei WangJun HuangFei CuiTao XuFleming Y M LureMeixiao ZhanYuanyi HuangQiang YangQionghai DaiWenhua LiangJianxing HeNanshan ZhongPublished in: European radiology (2022)
• CoviDet could diagnose COVID-19 based on chest CT with high consistency; this outperformed the radiologist's assessment. Its auto-segmentation analyses co-related well with those by radiologists and could potentially monitor and predict a patient's clinical course if serial CT assessments are available. It can be integrated into the federated learning framework. • CoviDet can be used as an adjunct to aid clinicians with the CT diagnosis of COVID-19 and can potentially be used for disease monitoring; federated learning can potentially open opportunities for global collaboration.