From the Department of Radiology, NYU Grossman School of Medicine, New York, NY.
Siemens Medical Solutions USA, Princeton, NJ.
Invest Radiol. 2023 Jun 1;58(6):405-412. doi: 10.1097/RLI.0000000000000951. Epub 2023 Jan 18.
Detection of rotator cuff tears, a common cause of shoulder disability, can be time-consuming and subject to reader variability. Deep learning (DL) has the potential to increase radiologist accuracy and consistency.
The aim of this study was to develop a prototype DL model for detection and classification of rotator cuff tears on shoulder magnetic resonance imaging into no tear, partial-thickness tear, or full-thickness tear.
This Health Insurance Portability and Accountability Act-compliant, institutional review board-approved study included a total of 11,925 noncontrast shoulder magnetic resonance imaging scans from 2 institutions, with 11,405 for development and 520 dedicated for final testing. A DL ensemble algorithm was developed that used 4 series as input from each examination: fluid-sensitive sequences in 3 planes and a sagittal oblique T1-weighted sequence. Radiology reports served as ground truth for training with categories of no tear, partial tear, or full-thickness tear. A multireader study was conducted for the test set ground truth, which was determined by the majority vote of 3 readers per case. The ensemble comprised 4 parallel 3D ResNet50 convolutional neural network architectures trained via transfer learning and then adapted to the targeted domain. The final tear-type prediction was determined as the class with the highest probability, after averaging the class probabilities of the 4 individual models.
The AUC overall for supraspinatus, infraspinatus, and subscapularis tendon tears was 0.93, 0.89, and 0.90, respectively. The model performed best for full-thickness supraspinatus, infraspinatus, and subscapularis tears with AUCs of 0.98, 0.99, and 0.95, respectively. Multisequence input demonstrated higher AUCs than single-sequence input for infraspinatus and subscapularis tendon tears, whereas coronal oblique fluid-sensitive and multisequence input showed similar AUCs for supraspinatus tendon tears. Model accuracy for tear types and overall accuracy were similar to that of the clinical readers.
Deep learning diagnosis of rotator cuff tears is feasible with excellent diagnostic performance, particularly for full-thickness tears, with model accuracy similar to subspecialty-trained musculoskeletal radiologists.
肩袖撕裂是导致肩部残疾的常见原因，其检测较为耗时，且易受读者差异的影响。深度学习（DL）有可能提高放射科医生的准确性和一致性。
本研究旨在开发一种用于检测和分类磁共振成像（MRI）肩袖撕裂的 DL 模型，包括无撕裂、部分厚度撕裂和全厚度撕裂。
这项符合《健康保险流通与责任法案》和机构审查委员会规定的研究共纳入了来自 2 家机构的 11925 例非对比性肩部 MRI 扫描，其中 11405 例用于开发，520 例用于最终测试。使用每个检查的 4 个序列作为输入来开发 DL 集成算法：3 个平面的液体敏感序列和矢状斜 T1 加权序列。放射科报告作为训练的金标准，分为无撕裂、部分撕裂或全厚度撕裂。对测试集的金标准进行了多读者研究，每个病例由 3 位读者进行多数投票确定。集成由 4 个并行的 3D ResNet50 卷积神经网络架构组成，通过迁移学习进行训练，然后适应目标领域。在对 4 个独立模型的类别概率进行平均后，确定最终撕裂类型的预测结果为概率最高的类别。
冈上肌腱、冈下肌腱和肩胛下肌腱撕裂的总体 AUC 分别为 0.93、0.89 和 0.90。全层冈上肌腱、冈下肌腱和肩胛下肌腱撕裂的模型表现最佳，AUC 分别为 0.98、0.99 和 0.95。对于冈下肌腱和肩胛下肌腱撕裂，多序列输入的 AUC 高于单序列输入，而对于冈上肌腱撕裂，冠状斜位液体敏感和多序列输入的 AUC 相似。撕裂类型和总体准确性的模型准确率与临床读者相似。
使用深度学习诊断肩袖撕裂是可行的，具有出色的诊断性能，特别是对于全层撕裂，其模型准确率与专门从事肌肉骨骼放射学的亚专科医生相似。
