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A Method to Estimate Cadaveric Femur Cortical Strains During Fracture Testing Using Digital Image Correlation

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Prediction of osteoporosis from simple hip radiography using deep learning algorithm.

Ryoungwoo Jang¹, Jae Ho Choi², Namkug Kim³

¹Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea.

Scientific Reports

|October 8, 2021

Summary

This study developed a deep learning model using simple hip X-rays to predict osteoporosis in women aged 55 and older. The AI tool shows promise as an accessible screening method for osteoporosis.

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Last Updated: Oct 17, 2025

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Imaging of the Microstructural Failure Mechanism in the Human Hip

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Published on: September 29, 2023

Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis diagnosis but is not ideal for widespread screening.
There is a need for accessible and cost-effective osteoporosis screening methods.

Purpose of the Study:

To develop and validate a deep learning algorithm for predicting osteoporosis using simple hip radiography.
To assess the feasibility of using AI to screen for osteoporosis from standard X-ray images.

Main Methods:

A deep neural network (DNN) model based on VGG16 and nonlocal neural network was developed.
1001 hip radiographs from female patients (≥55 years) with matched DXA data were used for training, validation, and testing.
External validation was performed on 117 additional datasets.

Main Results:

The DNN model achieved an 81.2% accuracy, 91.1% sensitivity, and 68.9% specificity in internal validation.
The model demonstrated an Area Under the ROC Curve (AUC) of 0.867, indicating good predictive performance.
External validation showed 71.8% accuracy and an AUC of 0.700, confirming its utility.

Conclusions:

Simple hip radiography combined with a deep learning model can effectively predict osteoporosis.
This AI-driven approach offers a potential, user-friendly screening tool for osteoporosis in clinical settings.