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Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.
Wang, Robin; Jiao, Zhicheng; Yang, Li; Choi, Ji Whae; Xiong, Zeng; Halsey, Kasey; Tran, Thi My Linh; Pan, Ian; Collins, Scott A; Feng, Xue; Wu, Jing; Chang, Ken; Shi, Lin-Bo; Yang, Shuai; Yu, Qi-Zhi; Liu, Jie; Fu, Fei-Xian; Jiang, Xiao-Long; Wang, Dong-Cui; Zhu, Li-Ping; Yi, Xiao-Ping; Healey, Terrance T; Zeng, Qiu-Hua; Liu, Tao; Hu, Ping-Feng; Huang, Raymond Y; Li, Yi-Hui; Sebro, Ronnie A; Zhang, Paul J L; Wang, Jianxin; Atalay, Michael K; Liao, Wei-Hua; Fan, Yong; Bai, Harrison X.
  • Wang R; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
  • Jiao Z; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Yang L; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Choi JW; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Xiong Z; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA, 19104, USA.
  • Halsey K; Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, China.
  • Tran TML; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.
  • Pan I; Warren Alpert Medical School at Brown University, Providence, RI, USA.
  • Collins SA; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
  • Feng X; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.
  • Wu J; Warren Alpert Medical School at Brown University, Providence, RI, USA.
  • Chang K; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.
  • Shi LB; Warren Alpert Medical School at Brown University, Providence, RI, USA.
  • Yang S; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.
  • Yu QZ; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.
  • Liu J; Carina Medical, Carina, Australia.
  • Fu FX; Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China.
  • Jiang XL; Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Wang DC; Department of Radiology, Yongzhou Central Hospital, Yongzhou, China.
  • Zhu LP; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
  • Yi XP; Department of Radiology, The First Hospital of Changsha, Changsha, China.
  • Healey TT; Department of Radiology, Changde Second People's Hospital, Changde, China.
  • Zeng QH; Department of Radiology, Yiyang City Center Hospital, Yiyang, China.
  • Liu T; Department of Radiology, Affiliated Nan Hua Hospital, University of South China, Hengyang, China.
  • Hu PF; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
  • Huang RY; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
  • Li YH; Department of Radiology, Xiangya Hospital, Central South University, Changsha, China.
  • Sebro RA; Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.
  • Zhang PJL; Department of Radiology, Loudi Central Hospital, Loudi, China.
  • Wang J; Brown University School of Public Health, Providence, RI, USA.
  • Atalay MK; Department of Radiology, Chenzhou Second People's Hospital, Chenzhou, China.
  • Liao WH; Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
  • Fan Y; Department of Radiology, Zhuzhou Central Hospital, Zhuzhou, China.
  • Bai HX; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Eur Radiol ; 32(1): 205-212, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1293361
ABSTRACT

OBJECTIVES:

Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.

METHODS:

An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19.

RESULTS:

A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001).

CONCLUSIONS:

Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Controlled clinical trial / Observational study / Prognostic study / Randomized controlled trials / Risk factors Limits: Humans Language: English Journal: Eur Radiol Journal subject: Radiology Year: 2022 Document Type: Article Affiliation country: S00330-021-08049-8

Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Controlled clinical trial / Observational study / Prognostic study / Randomized controlled trials / Risk factors Limits: Humans Language: English Journal: Eur Radiol Journal subject: Radiology Year: 2022 Document Type: Article Affiliation country: S00330-021-08049-8