Osteoporosis Detection Using Artificial Intelligence

The global demographic shift towards an aging population has intensified the prevalence of diseases like osteoporosis, characterized by fragile bones and heightened fracture risk, particularly in the spine, hips, and wrists. This condition, more common in women, results from low bone mass and poor structure, leading to weakened bones and increased susceptibility to fractures. While Dual Energy X-ray Absorptiometry (DEXA) has been a traditional diagnostic tool, its limited availability, cost, and radiation exposure pose challenges. However, Computer- Aided Diagnosis (CAD) has elevated diagnostic accuracy. In modern medical education, Deep Learning, Machine Learning, and Artificial Intelligence have revolutionized healthcare. These technologies enable precise osteoporosis diagnosis by analyzing clinical data and images using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). By combining advanced algorithms with medical expertise, these systems offer automated detection and diagnosis, improving early intervention and reducing osteoporosis's impact on individuals and healthcare systems. This integration underscores the critical role of technology in healthcare advancement. Keywords: AI, deep learning, feature extraction, ML, osteoporosis