Opportunistic osteoporosis screening using chest radiographs with deep learning: Development and external validation with a cohort dataset.

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Abstract

Osteoporosis is a common, but silent disease until it is complicated by fractures which are associated morbidity and mortality. Over the past few years, although deep learning-based disease diagnosis on chest radiographs has yielded promising results, osteoporosis screening remains unexplored. Paired data with 13,026 chest radiographs and dual energy X-ray absorptiometry (DXA) results from the Health Screening and Promotion Center of Asan Medical Center, between 2012 and 2019, were used as the primary dataset in this study. For the external test, we additionally used the Asan osteoporosis cohort dataset (1,089 chest radiographs, 2010 and 2017). Using one of well-performed deep learning models, we trained OsPor-screen model with labels defined by DXA based diagnosis of osteoporosis (lumbar spine, femoral neck, or total hip T-score ≤ -2.5) in a supervised learning manner. OsPor-screen model was assessed in the internal and external test sets. We performed sub-studies for evaluating the effect of various anatomical subregions and image sizes of input images. OsPor-screen model performances including sensitivity, specificity, and area under the curve (AUC) were measured in the internal and external test sets. In addition, visual explanations of the model to predict each class were expressed in gradient-weighted class activation maps (Grad-CAMs). OsPor-screen model showed promising performances. Osteoporosis screening with OsPor-screen model achieved an AUC of 0.91 (95% confidence interval (CI): 0.90-0.92) and an AUC of 0.88 (95% CI: 0.85-0.90) in the internal and external test set, respectively. Even though the medical relevance of these average Grad-CAMs is unclear, these results suggest that a deep learning-based model using chest radiographs could have the potential to be used opportunistic automated screening of patients with osteoporosis in clinical settings. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.