Image-based Machine Learning Approaches for the Early Detection of Osteoporosis: A Predictive Model Development and Evaluation Study

This study aims to enhance early osteoporosis detection through the development and evaluation of image-based machine learning models, providing a more accurate and efficient diagnostic framework for timely medical intervention. The study focuses on leveraging predictive models, including Support Vector Machines (SVM), Random Forest (RF), and Logistic Regression (LR), to analyze medical images such as X-ray images and DEXA scans obtained from public and clinical datasets. These images undergo preprocessing techniques such as noise reduction, contrast enhancement, and edge sharpening to improve feature extraction. Critical osteoporosis-related patterns are identified using texture analysis, wavelet transforms, and Principal Component Analysis (PCA). The models are trained and validated using a stratified 80-20 train-test split, with hyperparameter tuning performed via grid search and cross-validation. Performance evaluation is conducted based on accuracy, precision, recall, F1-score, and the area under the Receiver Operating Characteristic (ROC) curve. The Random Forest model achieved the highest accuracy of 92.3%, followed by SVM at 89.7% and Logistic Regression at 86.5%. Additionally, the RF model demonstrated superior sensitivity (93.1%) and specificity (91.5%), with an AUC of 0.96. Feature importance analysis revealed that trabecular patterns and cortical thickness are highly significant in osteoporosis diagnosis. These findings highlight the potential of image-based machine learning as a fast, contactless, and effective diagnostic tool for osteoporosis, facilitating early intervention and improved patient outcomes. Future research will explore deep learning techniques for enhanced feature extraction and further accuracy improvements.