Automated Screening of Hip X-rays for Osteoporosis by Singh’s Index Using Machine Learning Algorithms

Abstract

Introduction

Osteoporosis is a significant and growing global public health problem, projected to increase in the next decade. The Singh Index (SI) is a simple, semi-quantitative evaluation tool for diagnosing osteoporosis with plain hip radiographs based on the visibility of the trabecular pattern in the proximal femur. This work aims to develop an automated tool to diagnose osteoporosis using SI of hip radiograph images with the help of machine learning algorithms.

Methods

We used 830 hip X-ray images collected from Indian men and women aged between 20 and 70 which were annotated and labeled for appropriate SI. We employed three state-of-the-art machine learning algorithms—Vision Transformer (ViT), MobileNet-V3, and a Stacked Convolutional Neural Network (CNN)—for image pre-processing, feature extraction, and automation. Each algorithm was evaluated and compared for accuracy, precision, recall, and generalization capabilities to diagnose osteoporosis.

Results

The ViT model achieved an overall accuracy of 62.6% with macro-averages of 0.672, 0.597, and 0.622 for precision, recall, and F1 score, respectively. MobileNet-V3 presented a more encouraging accuracy of 69.6% with macro-averages for precision, recall, and F1 score of 0.845, 0.636, and 0.652, respectively. The stacked CNN model demonstrated the strongest performance, achieving an accuracy of 93.6% with well-balanced precision, recall, and F1-score metrics.

Conclusion

The superior accuracy, precision-recall balance, and high F1-scores of the stacked CNN model make it the most reliable tool for screening radiographs and diagnosing osteoporosis using the SI.