EFR-Net: Enhanced Fracture Prediction in Osteoporosis with U-Net-Based Analysis

Abstract

Osteoporosis, a prevalent bone disease, is characterized by the equation , where  is bone density,  is maximum bone density, and  is osteoporosis rate. Conventional imaging techniques, governed by the formula where accuracy is,  is image thresholding, and  is scan resolution), often yield a detection accuracy of merely 75%. In this work, we introduce the EFR-Net: a U-Net-based deep learning model. Its efficacy is represented by the equation , where  is the new accuracy,  is the fraction of fracture-prone regions detected,  is the Dice coefficient, and  is the noise reduction factor. Leveraging a comprehensive dataset of 10,000 bone scans, our model, adhering to the above equation, achieved a commendable accuracy rate of 89%. This translates to a mathematical improvement represented by , yielding a 14% enhancement over traditional methods. Moreover, the reduction in false negatives, a critical metric in medical diagnoses, can be quantified by , where  and  are the old and new false negatives respectively. EFR-Net's innovative approach and promising results underline its potential in revolutionizing osteoporosis-related fracture prediction, offering a robust bridge between computational advancements and clinical necessities.