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Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.
Bai, Harrison X; Wang, Robin; Xiong, Zeng; Hsieh, Ben; Chang, Ken; Halsey, Kasey; Tran, Thi My Linh; Choi, Ji Whae; Wang, Dong-Cui; Shi, Lin-Bo; Mei, Ji; Jiang, Xiao-Long; Pan, Ian; Zeng, Qiu-Hua; Hu, Ping-Feng; Li, Yi-Hui; Fu, Fei-Xian; Huang, Raymond Y; Sebro, Ronnie; Yu, Qi-Zhi; Atalay, Michael K; Liao, Wei-Hua.
  • Bai HX; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Wang R; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Xiong Z; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Hsieh B; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Chang K; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Halsey K; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Tran TML; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Choi JW; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Wang DC; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Shi LB; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Mei J; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Jiang XL; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Pan I; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Zeng QH; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Hu PF; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Li YH; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Fu FX; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Huang RY; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Sebro R; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Yu QZ; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Atalay MK; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
  • Liao WH; From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvan
Radiology ; 296(3): E156-E165, 2020 09.
Article in English | MEDLINE | ID: covidwho-729427
Semantic information from SemMedBD (by NLM)
1. Performance PROCESS_OF radiologist
Subject
Performance
Predicate
PROCESS_OF
Object
radiologist
2. Pneumonia PROCESS_OF Patients
Subject
Pneumonia
Predicate
PROCESS_OF
Object
Patients
3. Performance PROCESS_OF radiologist
Subject
Performance
Predicate
PROCESS_OF
Object
radiologist
4. Pneumonia PROCESS_OF Patients
Subject
Pneumonia
Predicate
PROCESS_OF
Object
Patients
ABSTRACT
Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 721 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI] 90%, 98%), a sensitivity of 95% (95% CI 83%, 100%), and a specificity of 96% (95% CI 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI 82%, 90%), a sensitivity of 89% (95% CI 81%, 94%), and a specificity of 86% (95% CI 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Artificial Intelligence / Tomography, X-Ray Computed / Coronavirus Infections / Radiologists Type of study: Diagnostic study / Observational study / Prognostic study Limits: Adolescent / Adult / Aged / Child / Child, preschool / Female / Humans / Infant / Male / Middle aged Country/Region as subject: North America / Asia Language: English Journal: Radiology Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Artificial Intelligence / Tomography, X-Ray Computed / Coronavirus Infections / Radiologists Type of study: Diagnostic study / Observational study / Prognostic study Limits: Adolescent / Adult / Aged / Child / Child, preschool / Female / Humans / Infant / Male / Middle aged Country/Region as subject: North America / Asia Language: English Journal: Radiology Year: 2020 Document Type: Article