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Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study.
Sun, Ju; Peng, Le; Li, Taihui; Adila, Dyah; Zaiman, Zach; Melton-Meaux, Genevieve B; Ingraham, Nicholas E; Murray, Eric; Boley, Daniel; Switzer, Sean; Burns, John L; Huang, Kun; Allen, Tadashi; Steenburg, Scott D; Gichoya, Judy Wawira; Kummerfeld, Erich; Tignanelli, Christopher J.
Afiliación
  • Sun J; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Peng L; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Li T; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Adila D; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Zaiman Z; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Melton-Meaux GB; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Ingraham NE; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Murray E; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Boley D; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Switzer S; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Burns JL; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Huang K; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Allen T; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Steenburg SD; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Gichoya JW; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Kummerfeld E; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
  • Tignanelli CJ; Department of Computer Science and Engineering (J.S., L.P., T.L., D.A., D.B.), Institute for Health Informatics (G.B.M.M., E.K., C.J.T.), Department of Surgery (G.B.M.M., C.J.T.), Department of Medicine, Division of Pulmonary and Critical Care (N.E.I.), Department of Medicine (S.S.), and Department
Radiol Artif Intell ; 4(4): e210217, 2022 Jul.
Article en En | MEDLINE | ID: mdl-35923381
Purpose: To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs. Materials and Methods: A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists. Results: Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0-0.8] vs 0.0 [IQR, 0.0-0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both). Conclusion: AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction.Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is available for this article.. © RSNA, 2022.
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Radiol Artif Intell Año: 2022 Tipo del documento: Article
Texto completo
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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Radiol Artif Intell Año: 2022 Tipo del documento: Article