Background:Repair of rotator cuff tear is not always feasible, depending on the severity. Although several studies have investigated factors related to reparability and various methods to predict it, inconsistent scoring methods and a lack of validation have hindered the utility of these methods.Purpose:To develop machine learning models to predict the reparability of rotator cuff tears, compare them with previous scoring systems, and provide an accessible online model.Study Design:Cohort study; Level of evidence, 3.Methods:Arthroscopic rotator cuff repairs for tears with both anteroposterior and mediolateral diameters >1 cm on preoperative magnetic resonance imaging were included and divided into a training set (70%) and an internal validation set (30%). For external validation, rotator cuff repairs performed by 2 different surgeons were included in a test set. Machine learning models and a newly adjusted scoring system were developed using the training set. The performance of the models including the adjusted scoring system and 2 previous scoring systems were compared using the test set. The performance was assessed using metrics such as the area under the receiver operating characteristic curve (AUROC) and compared using the net reclassification improvement based on the adjusted scoring system.Results:A total of 429 patients were included for the training and internal validation set, and 112 patients were included for the test set. An elastic-net logistic regression demonstrated the best performance, with an AUROC of 0.847 and net reclassification improvement of 0.071, compared with the adjusted scoring system in the test set. The AUROC of the adjusted scoring system was 0.786, and the AUROCs of the previous scoring systems were 0.757 and 0.687. The elastic-net logistic regression was transformed into an accessible online model.Conclusion:The performance of the machine learning model, which provides a probability estimation for rotator cuff reparability, is comparable with that of the adjusted scoring system. Nevertheless, when deploying prediction models beyond the original cohort, regardless of whether they rely on machine learning or scoring systems, clinicians should exercise caution and not rely solely on the output of the model.

中文翻译：

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预测肩袖撕裂的可修复性：机器学习和与以前的评分系统的比较


背景： 肩袖撕裂的修复并不总是可行的，具体取决于严重程度。尽管有几项研究调查了与可修复性相关的因素和各种预测方法，但不一致的评分方法和缺乏验证阻碍了这些方法的实用性。目的： 开发机器学习模型来预测肩袖撕裂的可修复性，将其与以前的评分系统进行比较，并提供一个可访问的在线模型。研究设计： 队列研究;证据等级， 3.方法： 纳入术前磁共振成像前后和中外径 >1 cm 撕裂的关节镜肩袖修复术，并分为训练集 （70%） 和内部验证集 （30%）。为了进行外部验证，由 2 名不同的外科医生进行的肩袖修复被包含在一个测试集中。机器学习模型和新调整的评分系统是使用训练集开发的。使用测试集比较模型的性能，包括调整后的评分系统和 2 个以前的评分系统。使用受试者工作特征曲线下面积 （AUROC） 等指标评估性能，并使用基于调整后的评分系统的净重分类改进进行比较。结果： 训练和内部验证集共纳入 429 例患者，测试集纳入 112 例患者。弹性网 logistic 回归显示性能最佳，与测试集中调整后的评分系统相比，AUROC 为 0.847，净重分类改进 0.071。调整后评分系统的 AUROC 为 0.786，之前评分系统的 AUROC 分别为 0.757 和 0.687。 elastic-net logistic 回归被转换为可访问的在线模型。结论： 为肩袖修复性提供概率估计的机器学习模型的性能与调整后的评分系统相当。然而，在将预测模型部署到原始队列之外时，无论它们是依赖于机器学习还是评分系统，临床医生都应该谨慎行事，而不仅仅是依赖模型的输出。