# Machine Learning to Recognise ACL Tears: A Systematic Review
## 3 Results  
The literature search resulted in 115 articles. After screening, 29 articles fulfilled all inclusion criteria (Tables 1 and 2, Figure 2). Of note, the search strategy did not include any restrictions on publication date. However, no relevant studies were published before 2017. Among the identified publications, 27 focused on MRI-based detection of ACL injuries, while only two examined X-ray images. Just one study addressed AI-based image recognition of PCL injuries. Almost all authors used artificial neural networks in the form of DL algorithms; all were level III studies. Statistical parameters used to determine the quality of the performance of the algorithms were sensitivity, specificity, positive predictive value, negative predictive value, area under the receiver operating characteristic curve (AUC ROC), dice similarity coefficient, F1-score, and intersection over union. The most frequently used statistical parameter was the sensitivity of the algorithm. The authors used datasets of very inhomogeneous size, ranging from 15 to 4086 MRI studies. Most authors used self-generated datasets, while some used pre-existing datasets such as the MRNet dataset published by the Stanford University Medical Center [11] or the dataset published by [PERSON] et al. [12]. Figure 2: PRISMA flow chart of the literature search. Abbreviations: ACL—anterior cruciate ligament; PCL—posterior cruciate ligament. Most MRI-based studies investigated the quality of AI image-based detection of ACL injury by using binary decision classes to distinguish between a normal ACL and a torn ACL in MRI images [13; 18; 19; 20; 21]. [PERSON] et al. also included images of abnormalities other than torn ACL in the regular ACL class [32]. Of those studies with binary decision classes, the CNN used by [PERSON] et al. performed best with a sensitivity of 0.98, specificity of 0.99, and AUC ROC of 1.0 on a self-generated dataset using 1806 MRI studies for training and validation and 201 MRI studies for testing [13]. In other studies of that design, sensitivity, specificity, and AUC ROC ranged between 0.87 [19] and 0.98 [13], 0.86 [23] and 0.99 [13] and 0.94 [29] and 1.0 [13]. Other authors used DL algorithms, which were trained to distinguish between more than two classes, e.g., \"normal ACL\", \"partially torn ACL\", and \"fully torn ACL\" [11; 12; 14; 15; 33; 34]. Among these studies, the U-Net CNN algorithm used by [PERSON] et al. performed best with a sensitivity, specificity, and AUC ROC of 0.98, 0.99, and 0.98 on a self-generated dataset using 827 MRI studies for training and validation and 276 MRI studies for testing [34]. In works by other authors, in addition to simply the condition of the native ACL, a possible postoperative reconstructed condition was included [15; 35]. One CNN designed to recognise reconstructed ACLs performed excellently with a sensitivity of 1.0 using 994 MRI studies for training and validation and 2248 MRI studies for testing [15]. Other studies took a different approach and focused on automatic image segmentation of the ACL or PCL region [17; 27; 36] or tear regions of the ACL [24; 37; 38]. For example, the DenseVNet CNN algorithm developed by [PERSON] et al. achieved a dice similarity coefficient of 0.964 for ACL region segmentation and 0.973 for the PCL region, using 25 MRI studies for training and validation and 15 MRI studies for testing [17]. Two studies also worked with X-rays in ACL patients [16; 39]: [PERSON] et al. [16] developed a model to identify ACL rupture on lateral knee radiographs, outperforming non-musculoskeletal radiologists using 1146 X-ray studies for training and validation and 287 X-ray studies for testing. [PERSON] et al. [39] developed a U-Net CNN algorithm to measure posterior tibial slope in short-leg lateral X-rays of ACL patients before and after ACL reconstruction. No significant differences were observed between manually performed posterior tibial slope measurements.