KneeFusionNet实现磁共振成像上膝关节韧带损伤的准确高效全面检测：一项多中心验证研究
简介
本研究基于3中心919例患者（开发集759例、外部测试集160例），构建并外部验证了多模态深度学习模型KneeFusionNet，用于在MRI上检测ACL、PCL、MCL、LCL损伤。外部验证中，四者ROC曲线下面积分别为0.888、0.867、0.862、0.874，均优于单模态及对照模型；AI辅助使低年资医生平均诊断准确率由0.818升至0.900，全体…
英文摘要
PURPOSE: To develop and externally validate KneeFusionNet, a multimodal deep learning model for detecting anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), medial collateral ligament (MCL), and lateral collateral ligament (LCL) injuries on knee magnetic resonance imaging (MRI), and to assess the impact of multimodal fusion and artificial intelligence (AI) assistance on diagnostic performance. METHODS: This 3-center retrospective study was conducted between April 2020 and August 2025. The injury group included patients who underwent knee MRI within 1 month before arthroscopy and had surgically confirmed ACL, PCL, MCL, or LCL injuries; controls had unremarkable MRI and physical examination findings. Two centers formed the development set, and the remaining center served as the external test set. DenseNet-based KneeFusionNet was developed and compared with 3 deep learning models. Diagnostic performance was assessed using the area under the receiver operating characteristic curve, and a reader study evaluated AI-assisted diagnostic performance. RESULTS: Overall, 919 patients were included: 759 in the development set and 160 in the external test set. Multimodal fusion outperformed single-modality approaches for all ligaments (all P < .05). On internal validation, KneeFusionNet achieved area under the receiver operating characteristic curves of 0.971 for ACL, 0.906 for PCL, 0.919 for MCL, and 0.924 for LCL. Corresponding external area under the receiver operating characteristic curves were 0.888, 0.867, 0.862, and 0.874. Sex-stratified analyses showed no consistent sex-related decrease in model performance. KneeFusionNet outperformed all comparison models on internal validation (all P < .05). AI assistance improved mean diagnostic accuracy for junior surgeons from 0.818 to 0.900 and reduced mean interpretation time by 14.73 seconds across all surgeons (all P < .05). CONCLUSIONS: KneeFusionNet detected ACL, PCL, MCL, and LCL injuries on MRI with high diagnostic performance and outperformed comparison models. AI assistance improved diagnostic accuracy for junior surgeons and reduced interpretation time for all surgeons. LEVEL OF EVIDENCE: Level III, retrospective case-control study.