Research Reports of the International Centre for Numerical Methods in Engineering (CIMNE) (2026). 1
Abstract
'''Objective:''' This study aimed to develop and evaluate a deep learning- based approach for automated estimation of hip bone mineral density (BMD), detection of osteoporosis and evaluation of fracture risk from routine hip radiographs. Further, the performance of multiple YOLOv9 variants was systematically compared with established transfer learning models to evaluate their potential for supporting osteoporosis as an image-based screening approach, where access to dual-energy X-ray absorptiometry (DXA) is limited. '''Methods:''' Routine hip x-ray Images were processed & analysed using multiple variants of YOLOv9 and established transfer learning models. The models were compared using standard evaluation metrics like accuracy, precision, recall, F1-score and Matthews Correlation Coefficient (MCC) for evaluating the model’s performance. In addition, patient-specific BMD measurements and the distribution of osteoporosis cases were further analysed to characterize bone health within the study cohort and determine the applicability of the proposed framework for automated osteoporosis screening. '''Results: '''Comparative evaluation showed that the YOLOv9 variants demonstrated superior predictive performance than the investigated transfer-learning models. Among, YOLOv9 variants the overall best performance was obtained by YOLOv9-E with an accuracy of 97.02%, precision of 96.71%, recall of 96.35%, F1-Score of 96.53% and MCC of 95.6%. the Strongest transfer learning model was efficientNetB0, achieving accuracy of 94.1% and MCC of 88.1%. At Patient level analysis mean BMD values ranged between 0.72 and 0.77g/cm2 with osteoporosis prevalence varied from 20% to 44% across the analysed population. '''Discussion:''' the strong performance of YOLOv9-E indicates the potential of object detection models may effectively learn radiographic patterns associated with low bone mass and osteoporosis. The complex hip anatomy and bone-related structural features explored & increased the model capacity with the improvement of YOLOv9 variants. These results support further investigation of routine hip radiographs for opportunistic osteoporosis screening although further external and clinical validation is required. '''Conclusion: '''The results suggest that YOLOv9 based analysis of routine hip radiographs demonstrate the effective and scalable approach for automated osteoporosis assessment. YOLOv9-E shows the strongest and balanced predictive performance relative to evaluated models. This approach may facilitate the early osteoporosis screening and clinical decision-making in healthcare settings where access to DXA is limited.
Abstract '''Objective:''' This study aimed to develop and evaluate a deep learning- based approach for automated estimation of hip bone mineral density (BMD), [...]
Automated Hip Osteoporosis Screening Using YOLOv9 and Data-Driven Models 
