OSTEOPOROSIS DETECTION TECHNIQUES: A COMPARATIVE REVIEW WITH EMPHASIS ON THE FEMUR

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.