Deep learning meets chest X-rays: a promising approach for predicting future compression fracture risk

Abstract

Background: Osteoporotic fractures are a significant global health concern, leading to disability and reduced quality of life. Existing diagnostic tools, such as dual-energy X-ray absorptiometry (DXA) and the Fracture Risk Assessment Tool, have limitations, such as dependence on structured datasets and difficulty identifying all high-risk individuals. Objectives: This study aimed to develop and validate an AI-enabled chest X-ray (AI-CXR) model for predicting osteoporotic fracture risk, offering a noninvasive, accessible alternative. Design: This is a retrospective study. Methods: This study analyzed 166,571 CXR from 78,548 patients in Taiwan, with internal validation on 31,977 X-rays and external validation on 36,677 X-rays. The datasets were divided into groups with and without T -scores. Radiological features such as costophrenic angle blunting and degenerative joint disease were extracted and incorporated into the predictive framework. The model’s performance was assessed using concordance indices, calibration curves, and stratified risk analyses, and compared to DXA-based T -scores. Results: The AI-CXR model demonstrated superior predictive accuracy compared to DXA, particularly for patients without T -scores (internal validation: concordance index 0.896 vs 0.829; external validation: 0.778 vs 0.818). Among high-risk groups identified by AI-CXR, the 5-year fracture incidence was significantly higher than in low-risk groups (internal: 2.6% vs 0.3%, hazard ratio (HR): 2.01; external: 3.5% vs 0.5%, HR: 2.34). Key radiological features were more prevalent in high-risk groups, including costophrenic angle blunting and degenerative joint disease. Stratified analysis revealed consistent performance across various demographic subgroups, such as gender and age categories. Conclusion: The AI-CXR model provides a cost-effective, noninvasive tool for osteoporotic fracture risk assessment, enabling improved early detection and personalized intervention across diverse clinical settings.

Ähnliche Arbeiten