Abstract: Osteoporosis is a progressive bone disorder that reduces bone density and deteriorates bone structure, significantly increasing fracture risk. Traditional diagnostic tools like DEXA scans are often expensive and inaccessible, particularly in rural or under-resourced regions. This project introduces an AI-based diagnostic system designed to detect osteoporosis using X-ray images and clinical data.
The system features three main modules: a CNN model trained to classify spine and knee X-rays, a machine learning-based clinical predictor using patient data, and a stage detection module for assessing disease severity. The CNN model achieved approximately 95% accuracy, while the clinical predictor using Gradient Boosting reached 92.01%.
A Flask-based web application provides an easy-to-use interface for patients and healthcare professionals. The system also delivers personalized treatment recommendations and optional doctor consultation links. By combining image analysis and clinical data evaluation, this hybrid approach offers a cost-effective, accessible, and accurate tool for early osteoporosis detection, especially beneficial in underserved areas.
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DOI:
10.17148/IJARCCE.2025.14662
