Abstract

Osteoporosis is a progressive disorder in which trabecular bones become porous, brittle, and prone to fractures due to reduced bone density and compromised microstructure, particularly in weight-bearing bones such as the femur. Early detection is critical for effective management and prevention of complications. This review provides a comprehensive comparison of current osteoporosis detection techniques, with a particular emphasis on the femoral region due to its clinical significance in predicting hip fractures. Conventional diagnostic methods like Dual-energy X-ray Absorptiometry (DEXA) and Quantitative Computed Tomography (QCT) remain the gold standards, offering high accuracy in bone mineral density (BMD) assessment. However, recent advancements have introduced novel approaches, including ultrasound, magnetic resonance imaging (MRI), and artificial intelligence (AI)-based image analysis, which aim to enhance diagnostic sensitivity and specificity, especially in early-stage detection. Additionally, the integration of machine learning algorithms in interpreting femoral scans has shown promise in identifying subtle structural changes undetectable by conventional methods. This review critically evaluates the strengths, limitations, and clinical applicability of each technique about femoral osteoporosis. By highlighting emerging trends and technological innovations, this study aims to guide clinicians and researchers toward more precise and proactive screening strategies, leading to better patient prognosis and decreased healthcare costs associated with osteoporotic fractures.

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