Development and Validation of an AI-Based Risk Prediction Model for Osteoporosis in Post-Menopausal Women
Year: 2025
Type: article
Abstract: 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.
Source: BioMed Target Journal
DOI: 10.59786/bmtj.317
Authors: Juhi Deshpande, Chanchal Kumar Singh
Institution: Banaras Hindu University
Language: English
Subfield: Orthopedics and Sports Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4412396189