Determination of Colles fracture risk index by X-ray images with the computer vision application

Background. Modelling a predictive risk index for Colles fractures using X-ray image analysis is a crucial application in orthopaedics since these fractures have essential health and economic burdens, particularly among the elderly. Previous research has established a correlation between decreased bone mineral density and the elevated risk of these fractures. This study purposed to assess a computer vision model for predicting the risk of Colles fractures occurrence upon any fall or physical stress on the wrist. Materials and methods. This model was obtained by analyzing quantitative characteristics extracted from forearm X-ray images. A predictive model was designed by the AIT project developer using Python 3.12, PHP and JS, html. The use of the AIT233 model was granted for this study under a non-profit scientific research license. Forearm X-ray datasets for the normal (without fracture, control group) and Colles fracture individuals were downloaded from a licensed repository. By implementing image thresholding, normalization and registration techniques, validated bone images were obtained and analyzed using computer vision algorithms. Results. A comparative analysis of forearm image intensity between subjects from the control group and patients with Colles fractures resulted in a 1.09 relative index of bone intensity (mean 82 ± 5 vs. 75 ± 6 pixels, respectively). The results showed statistically significant differences (p < 0.05), suggesting that image intensity is a potential predictor of fracture risk. A calculated risk score based on image intensity demonstrated a positive association with the occurrence of Colles fracture. Conclusions. The results provide the basis for developing a robust predictive tool that can aid in the prevention and management of Colles fractures.