Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19. Issue 29 (22nd July 2022)

• Record Type: Journal Article
• Authors: Li, Matthew D.; Arun, Nishanth T.; Aggarwal, Mehak; Gupta, Sharut; Singh, Praveer; Little, Brent P.; Mendoza, Dexter P.; Corradi, Gustavo C.A.; Takahashi, Marcelo S.; Ferraciolli, Suely F.; Succi, Marc D.; Lang, Min; Bizzo, Bernardo C.; Dayan, Ittai; Kitamura, Felipe C.; Kalpathy-Cramer, Jayashree
• Is Part Of: Medicine. Volume 101:Issue 29(2022)
• Page Start: e29587
• Publication Date: 2022-07-22
• Subjects: COVID-19 -- chest radiograph -- deep learning -- artificial intelligence -- generalizability; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000029587
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R ). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets ( R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets ( R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.