Advanced Osteoporosis Prediction from Knee X-rays via Residual Convolution and Recurrent Networks

Abstract

Osteoporosis is a progressive skeletal disorder marked by reduced bone density and structural deterioration, leading to increased fragility and susceptibility to fractures. The condition predominantly affects older adults and postmenopausal women, driven by hormonal changes that accelerate bone loss. Given its asymptomatic nature until a fracture occurs, early detection is crucial to prevent severe health complications and facilitate timely intervention. Traditional diagnostic tools, such as Dual-Energy X-ray Absorptiometry (DXA), though effective, are costly, less accessible in low-resource settings, and often invasive. This research addresses these limitations by proposing an advanced, non-invasive, and cost-efficient deep learning-based diagnostic model that analyzes knee X-ray images to identify osteoporosis at early stages. The model is particularly tailored for postmenopausal women, a high-risk demographic, aiming to improve screening coverage and diagnostic accuracy. Utilizing a publicly available dataset from Kaggle, the study introduces a hybrid deep learning framework combining ResNet-50, for spatial feature extraction, and Gated Recurrent Units (GRUs), for capturing temporal dependencies within imaging data. This fusion enables nuanced analysis of bone texture and morphology that correlates with osteoporosis progression. The proposed model achieved a classification accuracy of 95.65%, showcasing superior performance in distinguishing between normal and osteoporotic bone conditions. The novelty of this work lies in its integration of convolutional and recurrent layers to enhance feature learning from radiographic images, surpassing the limitations of existing single-architecture methods. Its implications in the healthcare domain are significant, as it not only offers a scalable tool for early osteoporosis detection but also supports proactive clinical decision-making, reducing the burden of fracture-related healthcare costs and improving quality of life, particularly for at-risk postmenopausal women.