Machine Learning and Deep Learning for Osteoporosis: A Review of Imaging-Based and Clinical Data Applications

Abstract

A significant public health concern, osteoporosis is a common skeletal condition marked by decreased bone mineral density (BMD) and increased fracture risk. Conventional diagnostical methods, including dual-energy X-ray absorptiometry (DXA), present inherent shortcomings in early detection and risk stratification. Breakthroughs in machine learning (ML) and deep learning (DL) in recent years have provided novel solutions to improve diagnostic accuracy and risk prediction, and efficiency of fracture detection. This review summarizes a integrated application of ML and DL approaches in osteoporosis research, highlighting their roles in predicting BMD, screening for osteoporosis, detecting fractures, and analyzing medical imaging data from CT, MRI, and X-ray. Studies have demonstrated that ML methods, such as LR and SVM, can accurately stratify individuals at risk of osteoporosis. Meanwhile, DL models, especially CNN and transfer learning techniques, have profoundly revolutionized the detection and assessment of osteoporotic changes in medical images. The integration of these technologies is poised to transform osteoporosis management, offering more personalized and effective patient care strategies.

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