Development and Validation of a Deep Learning System for the Detection of Nondisplaced Femoral Neck Fractures

Abstract

Hip fractures pose a significant challenge to healthcare systems due to their high costs and associated mortality rates, with femoral neck fractures accounting for nearly half of all hip fractures. This study addresses the challenge of diagnosing nondisplaced femoral neck fractures, which are often difficult to detect with standard radiographs, especially in elderly patients. This research evaluates a deep learning model that employs a convolutional neural network (CNN) within a ResNet framework, designed to enhance diagnostic accuracy for nondisplaced femoral neck fractures. The model was trained and validated on a dataset of 2032 hip radiographs from two hospitals, with additional external validation performed on datasets from other institutions. The AI model achieved an accuracy of 94.8% and an Area Under Curve of 0.991 on anteroposterior pelvic/hip radiographs, outperforming emergency physicians and delivering results comparable to expert physicians. External validation confirmed the model’s robust accuracy and generalizability across diverse datasets. This study underscores the potential of deep learning models to act as a supplementary tool in clinical settings, potentially reducing diagnostic errors and improving patient outcomes by facilitating a quicker diagnosis and treatment.