# Machine Learning to Recognise ACL Tears: A Systematic Review
## 4 Discussion  
The use of machine learning algorithms in managing cruciate ligament injuries is rapidly increasing. Notably, 36 of the 115 studies identified in our search were published in 2023 alone, with a marked rise in publication volume beginning in 2019. The retrieved articles appeared in journals spanning orthopaedic and trauma surgery, radiology, computer science, and health sciences, highlighting the interdisciplinary nature--and growing relevance--of this emerging field. Many studies have focused on using DL algorithms to detect cruciate ligament injuries in MRI scans. CNNs, a subset of DL, were employed by nearly all researchers addressing this task. CNNs are widely regarded as the most suitable tool for this purpose due to their ability to extract complex, abstract features from large datasets--specifically, the identification of injured cruciate ligaments within individual MR images from extensive MRI datasets [40,41]. This leads to efficient complex information processing and enables precise decisions and predictions to be made. In line with this observation, DL and CNNs are already the standard in other complex fields such as social media, chatbots, and self-driving cars [42,43]. A disadvantage of CNNs is that they require large datasets and high computational power, making training expensive and difficult for smaller datasets. They act as black-box models with limited interpretability and often struggle to capture global context in images. Last but not least, small input changes can significantly alter predictions [44]. CNNs can be further divided into subfamilies like U-Net, ResNet and Inception. ResNet uses residual connections to train very deep networks effectively but requires high computational power. It works well for general tasks but may be unnecessary for simpler problems [45]. Inception captures features at multiple scales, making it highly effective for object detection, although its implementation is complex. As a result, it reduces computational costs but incurs a longer inference time [46]. U-Net performs well with small datasets but is not ideal for classification. It is great for image segmentation and preserving fine details, but needs a lot of memory and is prone to overfitting [47]. Choosing the right model always depends on the task. Larger datasets improve a model's capacity to learn meaningful patterns and generalise more effectively to new data. In contrast, smaller datasets often result in overfitting, where the model memorises specific training examples instead of identifying the underlying features. Techniques such as dropout, data augmentation, and transfer learning can improve generalisation when data are limited. However, extremely large datasets can be costly and may not yield significant performance gains. A diverse and well-balanced dataset is more valuable than sheer volume alone. Ensuring high-quality, varied data are essential for building robust models that perform well in real-world scenarios [40,48]. The application of AI in the diagnosis and treatment of cruciate ligament injuries has not yet been fully integrated into routine clinical practice and remains largely experimental. Orthopaedic and trauma surgeons still use their clinical examination as the foundation for their decision-making regarding diagnostics and therapy, something that was thought never likely to be changed. Specific clinical tests of the ACL provide good sensitivity and specificity of 0.83 and 0.85 (95% CI, 0.77-0.88) for the anterior drawer test and 0.81 and 0.85 (95% CI, 0.73-0.87) for the Lachman's test, as shown in a meta-analysis by [PERSON] et al. [49]. The sensitivity and specificity of detecting cruciate ligament injuries increase when additionally using MR imaging: In a recent meta-analysis, [PERSON] et al. reported a combined sensitivity of 0.87 (95% CI, 0.84-0.90) and a specificity of 0.90 (95% CI, 0.88-0.92) [50]. The best DL CNN reviewed in this study was presented by [PERSON] et al.