Deep learning for MRI detection of sports injuries poses unique challenges. To address these difficulties, this study examines the feasibility and incremental benefit of several customized network architectures in evaluation of complete anterior cruciate ligament (ACL) tears. Two hundred sixty patients, ages 18-40, were identified in a retrospective review of knee MRIs obtained from September 2013 to March 2016. Half of the cases demonstrated a complete ACL tear (624 slices), the other half a normal ACL (3520 slices). Two hundred cases were used for training and validation, and the remaining 60 cases as an independent test set. For each exam with an ACL tear, coronal proton density non-fat suppressed sequence was manually annotated to delineate: (1) a bounding-box around the cruciate ligaments; (2) slices containing the tear. Multiple convolutional neural network (CNN) architectures were implemented including variations in input field-of-view and dimensionality. For single-slice CNN architectures, validation accuracy of a dynamic patch-based sampling algorithm (0.765) outperformed both cropped slice (0.720) and full slice (0.680) strategies. Using the dynamic patch-based sampling algorithm as a baseline, a five-slice CNN input (0.915) outperformed both three-slice (0.865) and single-slice (0.765) inputs. The final highest performing five-slice dynamic patch-based sampling algorithm resulted in independent test set AUC, sensitivity, specificity, PPV, and NPV of 0.971, 0.967, 1.00, 0.938, and 1.00. A customized 3D deep learning architecture based on dynamic patch-based sampling demonstrates high performance in detection of complete ACL tears with over 96% test set accuracy. A cropped field-of-view and 3D inputs are critical for high algorithm performance.

中文翻译：

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深度学习，用于检测完整的前交叉韧带撕裂。

用于运动损伤的MRI检测的深度学习提出了独特的挑战。为了解决这些困难，本研究考察了几种定制网络体系结构在评估完整的前交叉韧带（ACL）眼泪中的可行性和增加的收益。在2013年9月至2016年3月进行的膝部MRI回顾性研究中鉴定出260例年龄在18至40岁之间的患者。一半病例表现出ACL完全撕裂（624片），另一半表现出正常ACL（3520片） ）。200个案例用于培训和验证，其余60个案例作为独立测试集。对于每次有ACL撕裂的检查，均手动注释冠状质子密度非脂肪抑制序列，以描绘：（1）十字韧带周围的边界框；（2）含泪的切片。实现了多卷积神经网络（CNN）架构，其中包括输入视野和维度的变化。对于单切片CNN架构，基于动态补丁的采样算法（0.765）的验证准确性优于裁剪切片（0.720）和完整切片（0.680）策略。使用基于动态补丁的采样算法作为基准，五层CNN输入（0.915）优于三层（0.865）和单层（0.765）输入。最终性能最高的五层基于动态补丁的采样算法导致独立的测试集AUC，灵敏度，特异性，PPV和NPV为0.971、0.967、1.00、0.938和1.00。基于基于动态补丁的采样的定制3D深度学习架构展示了在检测完整ACL撕裂方面的高性能，测试集准确性超过96％。裁剪后的视场和3D输入对于提高算法性能至关重要。