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 wi

SUBMITTER: Chen KC 

PROVIDER: S-EPMC12304508 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature