Wang Lianxin, Zhang Ce, Wang Yaozong, Yue Xin, Liang Yunbang, Sun Naikun
Department of Orthopedics, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361003, China.
Department of Anesthesiology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361101, China.
Bioengineering (Basel). 2025 Apr 28;12(5):466. doi: 10.3390/bioengineering12050466.
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.
髋部骨折因其高昂的成本和相关死亡率，对医疗保健系统构成了重大挑战，其中股骨颈骨折占所有髋部骨折的近一半。本研究旨在应对诊断无移位股骨颈骨折的挑战，这种骨折通常难以通过标准X光片检测到，尤其是在老年患者中。本研究评估了一种深度学习模型，该模型在ResNet框架内采用卷积神经网络（CNN），旨在提高无移位股骨颈骨折的诊断准确性。该模型在来自两家医院的2032张髋部X光片数据集上进行了训练和验证，并在来自其他机构的数据集上进行了额外的外部验证。该人工智能模型在前后位骨盆/髋部X光片上的准确率达到了94.8%，曲线下面积为0.991，优于急诊医生，其结果与专家医生相当。外部验证证实了该模型在不同数据集上的稳健准确性和通用性。本研究强调了深度学习模型在临床环境中作为辅助工具的潜力，通过促进更快的诊断和治疗，有可能减少诊断错误并改善患者预后。
