Güngör Erdal, Vehbi Husam, Cansın Ahmetcan, Ertan Mehmet Batu
Department of Orthopaedics and Traumatology, Medipol University Esenler Hospital, Istanbul, Turkey.
Department of Radiology, Medipol University Esenler Hospital, Istanbul, Turkey.
Knee Surg Sports Traumatol Arthrosc. 2025 Feb;33(2):450-456. doi: 10.1002/ksa.12369. Epub 2024 Jul 17.
This study aims to evaluate the effectiveness of advanced deep learning models, specifically YOLOv8 and EfficientNetV2, in detecting meniscal tears on magnetic resonance imaging (MRI) using a relatively small data set.
Our data set consisted of MRI studies from 642 knees-two orthopaedic surgeons labelled and annotated the MR images. The training pipeline included MRI scans of these knees. It was divided into two stages: initially, a deep learning algorithm called YOLO was employed to identify the meniscus location, and subsequently, the EfficientNetV2 deep learning architecture was utilized to detect meniscal tears. A concise report indicating the location and detection of a torn meniscus is provided at the end.
The YOLOv8 model achieved mean average precision at 50% threshold (mAP@50) scores of 0.98 in the sagittal view and 0.985 in the coronal view. Similarly, the EfficientNetV2 model obtained area under the curve scores of 0.97 and 0.98 in the sagittal and coronal views, respectively. These outstanding results demonstrate exceptional performance in meniscus localization and tear detection.
Despite a relatively small data set, state-of-the-art models like YOLOv8 and EfficientNetV2 yielded promising results. This artificial intelligence system enhances meniscal injury diagnosis by generating instant structured reports, facilitating faster image interpretation and reducing physician workload.
Level III.
本研究旨在评估先进的深度学习模型，特别是YOLOv8和EfficientNetV2，在使用相对较小的数据集检测磁共振成像（MRI）半月板撕裂方面的有效性。
我们的数据集由642个膝盖的MRI研究组成——两名骨科医生对MR图像进行了标记和注释。训练流程包括这些膝盖的MRI扫描。它分为两个阶段：最初，使用一种名为YOLO的深度学习算法来识别半月板位置，随后，利用EfficientNetV2深度学习架构来检测半月板撕裂。最后提供一份简要报告，指出撕裂半月板的位置和检测结果。
YOLOv8模型在矢状面的50%阈值平均精度（mAP@50）得分是0.98，在冠状面是0.985。同样，EfficientNetV2模型在矢状面和冠状面的曲线下面积得分分别为0.97和0.98。这些出色的结果表明在半月板定位和撕裂检测方面具有卓越的性能。
尽管数据集相对较小，但YOLOv8和EfficientNetV2等先进模型取得了有前景的结果。这种人工智能系统通过生成即时结构化报告来增强半月板损伤诊断，便于更快地解读图像并减轻医生的工作量。
三级。
