AI-Based Osteoporosis Detection Using Clinical Bone Densitometry Data and Deep Learning Techniques

Osteoporosis is a progressive bone disease characterized by decreased bone mineral density (BMD) and an increased risk of fractures, especially among elderly individuals and postmenopausal women. Early detection is crucial to prevent severe complications and improve quality of life. In this project, we propose an AI-based Osteoporosis Detection System that analyzes both clinical patient data and Dual-Energy X-ray Absorptiometry (DXA) images to accurately classify bone health status into three categories: Normal, Osteopenia, and Osteoporosis. The methodology incorporates multiple machine learning models, including Random Forest, Support Vector Machine (SVM), Gradient Boosting, XGBoost, LightGBM, and a Multilayer Perceptron (MLP), trained using clinical attributes such as BMD, T-score, age group, and height. Additionally, a Convolutional Neural Network (CNN) is used to analyze DXA scan images for supportive prediction. The system automatically provides personalized lifestyle recommendations, including diet, exercise, and safety precautions based on the predicted condition.