深度学习辅助检测成人膝关节磁共振成像中的半月板和前交叉韧带联合撕裂：一项与关节镜检查相关性的交叉研究
Deep learning-assisted detection of meniscus and anterior cruciate ligament combined tears in adult knee magnetic resonance imaging: a crossover study with arthroscopy correlation.
作者信息
Behr Julien, Nich Christophe, D'Assignies Gaspard, Zavastin Catalin, Zille Pascal, Herpe Guillaume, Triki Ramy, Grob Charles, Pujol Nicolas
机构信息
Nantes Université, CHU Nantes, Clinique Chirurgicale Orthopédique et Traumatologique, Nantes, France.
Université Versailles Saint-Quentin-en-Yvelines, Centre hospitalier de Versailles - Hôpital Mignot, Service de Chirurgie Orthopédique et Traumatologique, Versailles, France.
出版信息
Int Orthop. 2025 Apr 28. doi: 10.1007/s00264-025-06531-2.
AIM
We aimed to compare the diagnostic performance of physicians in the detection of arthroscopically confirmed meniscus and anterior cruciate ligament (ACL) tears on knee magnetic resonance imaging (MRI), with and without assistance from a deep learning (DL) model.
METHODS
We obtained preoperative MR images from 88 knees of patients who underwent arthroscopic meniscal repair, with or without ACL reconstruction. Ninety-eight MR images of knees without signs of meniscus or ACL tears were obtained from a publicly available database after matching on age and ACL status (normal or torn), resulting in a global dataset of 186 MRI examinations. The Keros (Incepto, Paris) DL algorithm, previously trained for the detection and characterization of meniscus and ACL tears, was used for MRI assessment. Magnetic resonance images were individually, and blindly annotated by three physicians and the DL algorithm. After three weeks, the three human raters repeated image assessment with model assistance, performed in a different order.
RESULTS
The Keros algorithm achieved an area under the curve (AUC) of 0.96 (95% CI 0.93, 0.99), 0.91 (95% CI 0.85, 0.96), and 0.99 (95% CI 0.98, 0.997) in the detection of medial meniscus, lateral meniscus and ACL tears, respectively. With model assistance, physicians achieved higher sensitivity (91% vs. 83%, p = 0.04) and similar specificity (91% vs. 87%, p = 0.09) in the detection of medial meniscus tears. Regarding lateral meniscus tears, sensitivity and specificity were similar with/without model assistance. Regarding ACL tears, physicians achieved higher specificity when assisted by the algorithm (70% vs. 51%, p = 0.01) but similar sensitivity with/without model assistance (93% vs. 96%, p = 0.13).
CONCLUSIONS
The current model consistently helped physicians in the detection of medial meniscus and ACL tears, notably when they were combined.
LEVEL OF EVIDENCE
Diagnostic study, Level III.
目的
我们旨在比较医生在膝关节磁共振成像（MRI）上检测经关节镜证实的半月板和前交叉韧带（ACL）撕裂的诊断性能，有无深度学习（DL）模型的辅助。
方法
我们从接受关节镜半月板修复术（无论是否进行ACL重建）的患者的88个膝关节中获取术前MR图像。在根据年龄和ACL状态（正常或撕裂）匹配后，从一个公开可用的数据库中获取了98个无半月板或ACL撕裂迹象的膝关节的MR图像，从而形成了一个包含186次MRI检查的全局数据集。先前经过半月板和ACL撕裂检测与特征分析训练的Keros（Incepto，巴黎）DL算法用于MRI评估。磁共振图像由三名医生和DL算法分别进行盲法标注。三周后，三名评估者在模型辅助下以不同顺序重复图像评估。
结果
Keros算法在检测内侧半月板、外侧半月板和ACL撕裂时，曲线下面积（AUC）分别为0.96（95%CI 0.93，0.99）、0.91（95%CI 0.85，0.96）和0.99（95%CI 0.98，0.997）。在模型辅助下，医生在检测内侧半月板撕裂时实现了更高的敏感性（91%对83%，p = 0.04）和相似的特异性（91%对87%，p = 0.09）。对于外侧半月板撕裂，有无模型辅助时的敏感性和特异性相似。对于ACL撕裂，医生在算法辅助下实现了更高的特异性（70%对51%，p = 0.01），但有无模型辅助时的敏感性相似（93%对96%，p = 0.13）。
结论
当前模型持续帮助医生检测内侧半月板和ACL撕裂，尤其是当它们同时存在时。
证据水平
诊断性研究，III级。
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