Advances In Bone Health Analysis Using Deep Learning On Dexa Scan Images For Postmenopausal Osteoporosis

Abstract

Postmenopausal osteoporosis (POP), a progressive bone condition that puts patients at risk for fragility fractures, is characterized by decreasing bone mineral density (BMD) and altered bone microarchitecture. The ability of the conventional diagnostic instruments, which are mostly based on BMD measurements, to assess fracture risk and examine intricate bone structure patterns is restricted. To get over these restrictions, this research suggests a novel, medically informed deep learning model for thorough osteoporosis diagnosis and fracture analysis based on dual-energy X-ray absorptiometry (DEXA) pictures. The proposed model employs a multi-task learning framework that integrates osteoporosis severity evaluation, fracture point detection, and bone segmentation into a unified model. To achieve anatomically correct bone segmentation, a bidirectional U-Net is employed, and in scenarios where insufficient labeled samples are available, a self-supervised feature pretraining strategy enhances feature representation. A temporal dependency module is employed to model the retrieved features, and a spatial attention-based convolutional neural network is employed to enhance them in order to analyze subtle spatial and structural variations in regions of low bone mass. Additionally, a cross-attention fusion module is employed to integrate numerical BMD information with image-derived features to achieve a comprehensive and medically valid fracture risk evaluation that transcends BMD measurements. The TransResNet model based on attention enhances the reliability of models and makes it possible to estimate the confidence level of diagnostic predictions. High performance is proved by the experimental assessment of the model on the DEXA datasets, with accuracy of 98.96%, sensitivity of 98.26%, and specificity of 98.43%. The effectiveness of the model in identifying osteoporotic fractures and the severity of the disease is proved by the clinical validation of the model by orthopedic experts. The findings demonstrate that the suggested framework greatly improves diagnostic accuracy and reliability, demonstrating the potential of the most recent deep learning techniques to support early intervention, individualized treatment planning, and precise computer-aided osteoporosis diagnosis.