Orthopaedic Medical Center, The Second Hospital of Jilin University, Changchun, 130041, People's Republic of China.
Jilin Provincial Key Laboratory of Orhtopeadics, Changchun, People's Republic of China.
J Orthop Surg Res. 2023 Jun 13;18(1):426. doi: 10.1186/s13018-023-03909-z.
Accurately diagnosing supraspinatus tears based on magnetic resonance imaging (MRI) is challenging and time-combusting due to the experience level variability of the musculoskeletal radiologists and orthopedic surgeons. We developed a deep learning-based model for automatically diagnosing supraspinatus tears (STs) using shoulder MRI and validated its feasibility in clinical practice.
A total of 701 shoulder MRI data (2804 images) were retrospectively collected for model training and internal test. An additional 69 shoulder MRIs (276 images) were collected from patients who underwent shoulder arthroplasty and constituted the surgery test set for clinical validation. Two advanced convolutional neural networks (CNN) based on Xception were trained and optimized to detect STs. The diagnostic performance of the CNN was evaluated according to its sensitivity, specificity, precision, accuracy, and F1 score. Subgroup analyses were performed to verify its robustness, and we also compared the CNN's performance with that of 4 radiologists and 4 orthopedic surgeons on the surgery and internal test sets.
Optimal diagnostic performance was achieved on the 2D model, from which F1-scores of 0.824 and 0.75, and areas under the ROC curves of 0.921 (95% confidence interval, 0.841-1.000) and 0.882 (0.817-0.947) were observed on the surgery and internal test sets. For the subgroup analysis, the 2D CNN model demonstrated a sensitivity of 0.33-1.000 and 0.625-1.000 for different degrees of tears on the surgery and internal test sets, and there was no significant performance difference between 1.5 and 3.0 T data. Compared with eight clinicians, the 2D CNN model exhibited better diagnostic performance than the junior clinicians and was equivalent to senior clinicians.
The proposed 2D CNN model realized the adequate and efficient automatic diagnoses of STs, which achieved a comparable performance of junior musculoskeletal radiologists and orthopedic surgeons. It might be conducive to assisting poor-experienced radiologists, especially in community scenarios lacking consulting experts.
由于肌肉骨骼放射科医生和骨科医生经验水平的差异，基于磁共振成像（MRI）准确诊断冈上肌腱撕裂是一项具有挑战性且耗时的工作。我们开发了一种基于深度学习的模型，用于使用肩部 MRI 自动诊断冈上肌腱撕裂（ST），并验证了其在临床实践中的可行性。
共回顾性收集了 701 例肩部 MRI 数据（2804 幅图像）进行模型训练和内部测试。另外从接受肩部关节置换术的患者中收集了 69 例肩部 MRI（276 幅图像），构成了用于临床验证的手术测试集。训练和优化了两个基于 Xception 的先进卷积神经网络（CNN）以检测 ST。根据敏感性、特异性、精度、准确性和 F1 评分评估 CNN 的诊断性能。进行了亚组分析以验证其稳健性，我们还比较了 CNN 在手术和内部测试集上与 4 名放射科医生和 4 名骨科医生的性能。
在 2D 模型上获得了最佳诊断性能，在手术和内部测试集上的 F1 评分分别为 0.824 和 0.75，ROC 曲线下面积分别为 0.921（95%置信区间，0.841-1.000）和 0.882（0.817-0.947）。对于亚组分析，2D CNN 模型在手术和内部测试集上对不同程度的撕裂显示出 0.33-1.000 和 0.625-1.000 的敏感性，1.5 和 3.0 T 数据之间没有显著的性能差异。与 8 名临床医生相比，2D CNN 模型的诊断性能优于初级临床医生，与高级临床医生相当。
所提出的 2D CNN 模型实现了 ST 的充分和有效的自动诊断，其性能与初级肌肉骨骼放射科医生和骨科医生相当。它可能有助于辅助经验不足的放射科医生，尤其是在缺乏咨询专家的社区环境中。
