An Advanced Two-Stage Model with High Sensitivity and Generalizability for Prediction of Hip Fracture Risk Using Multiple Datasets

AI in healthcare
Published: arXiv: 2510.15179v1
Authors

Shuo Sun Meiling Zhou Chen Zhao Joyce H. Keyak Nancy E. Lane Jeffrey D. Deng Kuan-Jui Su Hui Shen Hong-Wen Deng Kui Zhang Weihua Zhou

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Abstract

Hip fractures are a major cause of disability, mortality, and healthcare burden in older adults, underscoring the need for early risk assessment. However, commonly used tools such as the DXA T-score and FRAX often lack sensitivity and miss individuals at high risk, particularly those without prior fractures or with osteopenia. To address this limitation, we propose a sequential two-stage model that integrates clinical and imaging information to improve prediction accuracy. Using data from the Osteoporotic Fractures in Men Study (MrOS), the Study of Osteoporotic Fractures (SOF), and the UK Biobank, Stage 1 (Screening) employs clinical, demographic, and functional variables to estimate baseline risk, while Stage 2 (Imaging) incorporates DXA-derived features for refinement. The model was rigorously validated through internal and external testing, showing consistent performance and adaptability across cohorts. Compared to T-score and FRAX, the two-stage framework achieved higher sensitivity and reduced missed cases, offering a cost-effective and personalized approach for early hip fracture risk assessment. Keywords: Hip Fracture, Two-Stage Model, Risk Prediction, Sensitivity, DXA, FRAX

Paper Summary

Problem
Hip fractures are a major public health concern, causing significant disability, mortality, and healthcare costs in older adults. However, current clinical tools for identifying individuals at high risk of hip fracture often lack sensitivity, missing many individuals who will eventually experience a fracture. This problem highlights the need for a more accurate and effective approach to predicting hip fracture risk.
Key Innovation
Researchers have developed a novel two-stage model for predicting hip fracture risk, which incorporates both clinical characteristics and imaging features from DXA scans. The model consists of two stages: a screening stage that uses clinical, demographic, lifestyle, cognitive, and functional factors to estimate the baseline risk of hip fracture, and an imaging stage that further refines the prediction using imaging features from DXA scans. This stepwise approach has been shown to improve sensitivity compared to traditional tools like T-score and FRAX.
Practical Impact
This research has the potential to improve outcomes and reduce the burden of osteoporosis and fractures in aging populations. By identifying individuals at high risk of hip fracture earlier and more accurately, healthcare providers can offer targeted interventions and lifestyle modifications to prevent fractures. This could lead to reduced healthcare costs, improved quality of life for older adults, and a decrease in the societal and economic burden of osteoporosis.
Analogy / Intuitive Explanation
Imagine trying to predict whether a car will break down based on its age, mileage, and maintenance history. A traditional approach might look at just the car's age and mileage, but this two-stage model is like adding a more detailed inspection of the car's engine and tires to the mix. This gives a more complete picture of the car's condition and allows for a more accurate prediction of whether it will break down. Similarly, this two-stage model for predicting hip fracture risk uses a combination of clinical and imaging data to get a more accurate picture of an individual's risk, allowing for earlier and more effective intervention.
Paper Information
Categories:
cs.LG physics.med-ph
Published Date:

arXiv ID:

2510.15179v1

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