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Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19. | LitMetric

AI Article Synopsis

  • The study aimed to enhance a deep learning model for assessing the severity of COVID-19 lung disease in chest radiographs (CXRs) by tuning it with outpatient data.
  • The model was evaluated on CXRs from various populations in the U.S. and Brazil, showing improved performance, particularly in U.S. hospitalized patients.
  • The findings suggest that training the model with data from a different patient group improved its effectiveness and allowed it to generalize well across multiple patient populations.

Article Abstract

Purpose: To improve 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.

Materials And Methods: 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 four 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.

Results: Tuning the deep learning model with outpatient data improved model performance in two 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.

Conclusions: Performance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523150PMC
http://dx.doi.org/10.1101/2020.09.15.20195453DOI Listing

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