ArticleTherapeutic advances in musculoskeletal disease2025
Deep learning meets chest X-rays: a promising approach for predicting future compression fracture risk.
Article in Therapeutic advances in musculoskeletal disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Advances in Imaging-Based Fracture Risk Assessment for Unlocking Latent Skeletal Fragility.Current osteoporosis reports · 2026Review
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8 authors.
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No grant is acknowledged in the PubMed record.
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 Results: The AI-CXR model demonstrated superior predictive accuracy compared to DXA, particularly for patients without 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.
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