Doctors Versus YOLO: Comparison Between YOLO Algorithm, Orthopedic and Traumatology Resident Doctors and General Practitioners on Detection of Proximal Femoral Fractures on X-ray Images with Multi Methods

Abstract

In the 1950s, the concept of artificial intelligence emerged, suggesting that machines could possess the ability to think and learn. In the 21st century, with advancements in GPUs and CPUs, deep learning has become an integral part of human life. Proximal femoral fractures are known to be one of the leading causes of mortality and injuries among the elderly population. This study aims to detect proximal femoral fractures in X-ray images and compare the success of using the YOLOv4 algorithm and provide decision support system within the diagnosis. To retrain the algorithm, more than 500 patients’ X-ray images were examined. Through data augmentation techniques, the initial set of 410 patients’ femur proximal fracture X-ray images was expanded to 820 images. After retraining the YOLO algorithm, two different groups were included for comparing the algorithm’s performance: orthopedic specialists and general practitioners. The results from these three groups were evaluated using specific criteria. The YOLOv4 model demonstrated an accuracy of 90.33%. In comparison, orthopedic and traumatology resident doctors achieved an accuracy of 91.42%, while the general practitioner group achieved an accuracy of 81.30%.