Development and Validation of an AI-Based Risk Prediction Model for Osteoporosis in Post-Menopausal Women

Osteoporosis is a leading cause of morbidity in postmenopausal women. Timely risk stratification remains challenging despite available screening tools. The aim is to develop and validate an AI-based predictive model for osteoporosis in postmenopausal women. A hybrid approach combining clinical expertise with machine learning algorithms was used to develop the model. Researchers used a dataset of 1,200 postmenopausal women (50-80 years) from three tertiary centers for training and validation. The final model demonstrated high accuracy (88.2%), sensitivity (90.1%), specificity (85.4%), and area under the receiver operating characteristic curve (AUC) (0.92). Key predictors included bone mineral density, BMI, years since menopause, serum vitamin D, and family history. This AI-based predictive model accurately identifies postmenopausal women at heightened risk for osteoporosis, enabling potential individualized treatment strategies and timely prophylactic measures. Further studies are needed to investigate model utility in larger, diverse cohorts.