Rotator cuff tear (RCT) is one of the most common shoulder injuries. When diagnosing RCT, skilled orthopedists visually interpret magnetic resonance imaging (MRI) scan data. For automated and accurate diagnosis of RCT, we propose a full 3D convolutional neural network (CNN) based method using deep learning. This 3D CNN automatically diagnoses the presence or absence of an RCT, classifies the tear size, and provides 3D visualization of the tear location. To train the 3D CNN, the Voxception-ResNet (VRN) structure was used. This architecture uses 3D convolution filters, so it is advantageous in extracting information from 3D data compared with 2D-based CNNs or traditional diagnosis methods. MRI data from 2,124 patients were used to train and test the VRN-based 3D CNN. The network is trained to classify RCT into five classes (None, Partial, Small, Medium, Large-to-Massive). A 3D class activation map (CAM) was visualized by volume rendering to show the localization and size information of RCT in 3D. A comparative experiment was performed for the proposed method and clinical experts by using randomly selected 200 test set data, which had been separated from training set. The VRN-based 3D CNN outperformed orthopedists specialized in shoulder and general orthopedists in binary accuracy (92.5% vs. 76.4% and 68.2%), top-1 accuracy (69.0% vs. 45.8% and 30.5%), top-1±1 accuracy (87.5% vs. 79.8% and 71.0%), sensitivity (0.94 vs. 0.86 and 0.90), and specificity (0.90 vs. 0.58 and 0.29). The generated 3D CAM provided effective information regarding the 3D location and size of the tear. Given these results, the proposed method demonstrates the feasibility of artificial intelligence that can assist in clinical RCT diagnosis.

肩袖撕裂 (RCT) 是最常见的肩部损伤之一。在诊断 RCT 时，熟练的骨科医生会直观地解释磁共振成像 (MRI) 扫描数据。为了自动和准确地诊断 RCT，我们提出了一种使用深度学习的基于全 3D 卷积神经网络 (CNN) 的方法。这个 3D CNN 自动诊断 RCT 的存在与否，对撕裂大小进行分类，并提供撕裂位置的 3D 可视化。为了训练 3D CNN，使用了 Voxception-ResNet (VRN) 结构。该架构使用 3D 卷积滤波器，因此与基于 2D 的 CNN 或传统诊断方法相比，它在从 3D 数据中提取信息方面具有优势。来自 2,124 名患者的 MRI 数据用于训练和测试基于 VRN 的 3D CNN。训练网络将 RCT 分为五类（无、部分、小、中型、大型到大型）。通过体积渲染将 3D 类激活图 (CAM) 可视化，以在 3D 中显示 RCT 的定位和大小信息。使用随机选择的 200 个测试集数据，对所提出的方法和临床专家进行了对比实验，这些数据已经从训练集中分离出来。基于 VRN 的 3D CNN 在二进制精度（92.5% 对 76.4% 和 68.2%）、top-1 精度（69.0% 对 45.8% 和 30.5%）、top-1 ± 1准确度（87.5% 对 79.8% 和 71.0%）、灵敏度（0.94 对 0.86 和 0.90）和特异性（0.90 对 0.58 和 0.29）。生成的 3D CAM 提供了有关撕裂的 3D 位置和大小的有效信息。鉴于这些结果，