# Machine Learning to Recognise ACL Tears: A Systematic Review
###### Abstract  
Machine learning-based tools are becoming increasingly popular in clinical practice. They offer new possibilities but are also limited in their reliability and accuracy. The present systematic review updates and discusses the existing literature regarding machine learning algorithm-based identification of cruciate ligament injury on radiographic images. PubMed was searched for articles containing machine learning algorithms related to cruciate ligament injury recognition. No additional filters or time constraints were used. All eligible studies were accessed by hand. From the 115 articles initially retrieved, 29 articles were finally included. Only one study included the posterior cruciate ligament (PCL). Deep learning algorithms in the form of convolutional neural networks (CNNs) were most frequently used. Many studies presented CNNs that identified binary decision classes of regular and torn anterior cruciate ligaments (ACLs) with a best sensitivity of 0.98, a specificity of 0.99, and an AUC ROC of 1.0. Other studies expanded the decision classes to partially torn ACLs or reconstructed ACLs, usually at the cost of sensitivity and specificity. Deep learning algorithms are excellent for identifying ACL injuries, tears, or postoperative status after reconstruction on MRI images. They are much faster but only sometimes better than the human reviewer. While the technology seems ready, barriers to ethical and legal issues and clinicians' refusals must be overcome to some extent. It can be firmly assumed that artificial intelligence will have a future contribution in the diagnosis of cruciate ligament injuries. machine learning; artificial intelligence; anterior cruciate ligament; artificial intelligence; deep learning; magnetic resonance imaging +
Footnote †: journal:constantly try to optimise patient treatment costs as healthcare systems are already exposed to a high burden. The incidence of an ACL rupture is reported to be 1 in 3500 humans per year in the USA, which makes it one of the most common knee injuries [3]. While clinical examination with, e.g., Lachman's test is a powerful tool for diagnosis, MR imaging of the knee joint is still considered the standard of care in preoperatively diagnosing ACL ruptures with superior sensitivity and specificity [4; 5]. Posterior cruciate ligament (PCL) injuries can result in significant functional impairment if left untreated. However, their incidence is reported to be considerably lower than that of anterior cruciate ligament (ACL) injuries [6]. In younger patients, complete rupture of the ACL or PCL is almost always an indication of surgery. Arthroscopic-assisted techniques allow most cases to be performed in an outpatient setting. In recent years, artificial intelligence (AI), machine learning, and its subset deep learning (DL) have proved their value in medical diagnostics and therapy by reducing workload and cost in healthcare systems worldwide [7]. A machine can imitate human intelligence, e.g., to solve complex problems based on logic or decision-making trees. DL algorithms can reliably distinguish pathological aspects from physiological findings on radiographic images of different organs [8] (Figure 1). In addition, DL methods are used to make predictions about clinical outcomes after various treatments, which helps to influence the physicians' choices beneficially [9]. The application of artificial intelligence (AI) has increasingly extended into orthopaedic surgery, particularly in assessing ACL and PCL injuries. Numerous studies have been published using deep learning (DL) algorithms to detect ACL injuries in radiographic images. This systematic review aims to update and critically examine the current literature on using machine learning algorithms for identifying ACL and PCL injuries in radiographic imaging. Figure 1: Simplified schematic representation of a multilayer convolutional neural network for detecting anterior cruciate ligament injuries on MRI images. Single images from different views of complete MRI datasets serve as input.