Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI arthrograms (MRAs). This study develops deep learning (DL) models to detect Bankart lesions on both standard MRIs and MRAs, aiming to improve diagnostic accuracy and reduce reliance on MRAs. We curated a dataset of 586 shoulder MRIs (335 standard, 251 MRAs) from 558 patients who underwent arthroscopy. Ground truth labels were derived from intraoperative findings, the gold standard for Bankart lesion diagnosis. Separate DL models for MRAs and standard MRIs were trained using the Swin Transformer architecture, pre-trained on a public knee MRI dataset. Predictions from sagittal, axial, and coronal views were ensembled to optimize performance. The models were evaluated on a 20% hold-out test set (117 MRIs: 46 MRAs, 71 standard MRIs). Bankart lesions were identified in 31.9% of MRAs and 8.6% of standard MRIs. The models achieved AUCs of 0.87 (86% accuracy, 83% sensitivity, 86% specificity) and 0.90 (85% accuracy, 82% sensitivity, 86% specificity) on standard MRIs and MRAs, respectively. These results match or surpass radiologist performance on our dataset and reported literature metrics. Notably, our model's performance on non-invasive standard MRIs matched or surpassed the radiologists interpreting MRAs. This study demonstrates the feasibility of using DL to address the diagnostic challenges posed by subtle pathologies like Bankart lesions. Our models demonstrate potential to improve diagnostic confidence, reduce reliance on invasive imaging, and enhance accessibility to care.

翻译：Bankart损伤，即肩胛盂前下盂唇撕裂，因其在标准MRI上的影像特征较为细微，诊断具有挑战性，通常需要依赖有创的磁共振关节造影（MRA）。本研究开发了深度学习（DL）模型，旨在通过标准MRI和MRA检测Bankart损伤，以提高诊断准确性并减少对MRA的依赖。我们收集了来自558名接受关节镜检查患者的586份肩关节MRI（335份标准MRI，251份MRA）。真实标签来源于术中发现的Bankart损伤，这是诊断的金标准。我们使用Swin Transformer架构，在一个公开的膝关节MRI数据集上进行预训练，分别针对MRA和标准MRI训练了独立的DL模型。通过集成矢状位、轴位和冠状位视图的预测结果来优化模型性能。模型在一个占20%的保留测试集（117份MRI：46份MRA，71份标准MRI）上进行了评估。Bankart损伤在MRA中的检出率为31.9%，在标准MRI中为8.6%。模型在标准MRI和MRA上分别取得了0.87（准确率86%，敏感性83%，特异性86%）和0.90（准确率85%，敏感性82%，特异性86%）的AUC值。这些结果与放射科医生在我们数据集上的表现以及文献报道的指标相当或更优。值得注意的是，我们的模型在无创标准MRI上的表现与放射科医生解读MRA的表现相当或更优。本研究证明了利用深度学习解决诸如Bankart损伤这类细微病变诊断挑战的可行性。我们的模型显示出提高诊断信心、减少对有创成像的依赖以及提升医疗服务可及性的潜力。