Objective: The primary aim of this study was to refine the accuracy and efficiency of hip fracture detection using a new computer vision model based on the YOLOv8 algorithm, thereby addressing the current limitations in diagnostic methodologies and improving dataset constraints.
Methods: We conducted a retrospective study using anterior-posterior (AP) hip radiographs collected from adult patients at University of Health Sciences Turkey, Şişli Hamidiye Etfal and University of Health Sciences Turkey, Sancaktepe Şehit Prof. Dr. İlhank Varank Training and Research Hospital between January 2021 and January 2023. A total of 676 radiographs were analyzed after applying classifications according to the AO/OTA system by orthopedic specialists. The dataset was divided into training, validation, and testing sets, and image augmentations were applied to enhance model training.
Results: The YOLOv8 model achieved a mean Average Precision at 0.5 IOU (mAP50) of 0.877 at the 99th epoch, demonstrating high diagnostic accuracy with a precision rate of 0.891 and recall of 0.797. These metrics indicate the model’s effectiveness in accurately detecting and classifying hip fractures.
Conclusion: This study presents a significant enhancement in the use of artificial intelligence for medical imaging, particularly in detecting and classifying hip fractures, thereby demonstrating the potential of AI to augment clinical decision-making. Further studies are recommended to expand the application scope and improve the model’s accuracy in various clinical environments.