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Opportunistic Osteoporosis Screening from Routine Knee Radiographs Using a Multi-Stage CNN Framework with External

Nitiphoom Sinnathakorn¹, Chanon Fahpinyo¹, Watcharaporn Cholamjiak^2,3

¹School of Medicine, University of Phayao, Phayao 56000, Thailand.

Journal of Clinical Medicine

|July 15, 2026

Summary

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This study developed an AI framework for osteoporosis detection using knee X-rays, showing that while accurate, external validation requires recalibration and threshold adjustments for reliable clinical use.

Area of Science:

Artificial Intelligence
Medical Imaging
Public Health

Background:

Osteoporosis poses a significant public health risk, leading to fractures and reduced quality of life.
Early detection through opportunistic screening of knee X-ray images using AI is a promising approach.

Purpose of the Study:

To develop and evaluate a multi-stage deep learning and machine learning framework for osteoporosis classification.
To emphasize external validation, calibration drift, and cross-domain generalization performance.

Main Methods:

Extracted deep features from knee X-rays using pretrained CNNs (ResNet18, EfficientNetB0, DenseNet121).
Classified features using ML models (Neural Network, Efficient Linear, SVM, Naive Bayes).
Investigated data augmentation and evaluated performance using accuracy, F1-score, AUC, and reliability calibration.

Keywords:

CNN framework external validation feature extraction knee X-ray osteoporosis

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Published on: September 28, 2017

Main Results:

EfficientNetB0 and DenseNet121 outperformed ResNet18.
External validation revealed calibration drift and class-prior mismatch.
Post-hoc recalibration and class-prior boosting improved performance on external datasets, especially for the Osteopenia class.

Conclusions:

The AI framework shows feasibility for osteoporosis classification from knee X-rays.
Adaptive recalibration and threshold optimization are crucial for maintaining performance across different domains.
Further validation on diverse clinical cohorts is needed for generalizability and clinical utility.