# Machine Learning to Recognise ACL Tears: A Systematic Review
## 4 Discussion
[13], which outperformed these values with a sensitivity of 0.98 and a specificity of 0.99 when recognising ACL tears on fat-saturated weighted MR images. Additionally,other models using binary decision classes (normal ACL, torn ACL) have outperformed combined clinical and MRI assessments by physicians in terms of sensitivity and specificity. However, the reality is far more complex than simple binary decision-making. When expanding the model to include other dimensions, sensitivity and specificity lowered in a relevant way. For example, the three-category model from [PERSON] et al. [11] had a sensitivity significantly lower than that of general radiologists, while the specificity was not considerably higher. [PERSON] et al. [29] also reached a specificity with their CNN that was significantly lower than the control group, while sensitivity was not significantly different. These findings are consistent with those of other studies [23; 31]. While DL-based detection of ACL injuries is not inherently superior to human analysis, its performance is highly context-dependent. Physical examination, MRI, or arthroscopy allow multiple and more complex questions to be answered simultaneously. In contrast, a machine learning algorithm must be extensively programmed and trained to decide between various classes. When conditions can be standardised, however, performance seems to remain relatively high in more complex decisions, as shown by [PERSON] et al. [34], who presented a CNN that differentiated between a standard ACL, a partially torn ACL, and a fully torn ACL with a sensitivity of 0.98 and a specificity of 0.99. [PERSON] et al. [15] even presented an algorithm that identified reconstructed ACLs on MRI images with a sensitivity and specificity of 1.0. This outstanding result might be due to the bundled, cylindric structure of commonly used autografts, which significantly differs from the physiological, more trapezoid, and flat shape of the ACL. Additional tasks are relatively easy for machine learning algorithms. Setting up complex machine learning algorithms might be time- and cost-intensive. Still, it must be remembered that a physician who reaches top statistical values for detecting ACL injuries in physical examinations or MRI images also needs several years of training. With the rising burden on the health care systems because of, e.g., demographic changes, effective use of the workforce becomes more critical. [PERSON] et al. [11] demonstrated that their DL model identified ACL tears at least ninety times faster than the clinical experts. While the human expert needed more than three hours to review 120 exams, the DL model completed the task in just two minutes. [PERSON] et al. [39] also highlighted that their DL tool for automated radiographic determination of posterior tibial slope in patients with ACL injuries only needed less than one minute to obtain angle measurements from ninety images. This raises an important question: Can healthcare systems afford to overlook the support of artificial intelligence? From a cost-benefit perspective, a machine-learning algorithm for detecting ACL tears may initially appear superior. However, as with many AI applications, ethical considerations must also be carefully addressed. A machine learning algorithm will always be faster than a human reviewer, making it significantly cheaper [51]. While practitioners represent ongoing expenses for healthcare systems, a machine learning tool is a one-time investment. Although advanced deep learning algorithms can cost over USD 1,000,000--significantly surpassing the average annual salary of a practitioner--they can perform tasks up to ninety times faster, making them cost-effective even in the short term [39]. Ultimately, the true measure of cost-effectiveness lies in the benefit to the patient. If AI underperforms compared to a human reviewer regarding diagnostic quality, cost becomes a secondary concern in a healthcare system prioritising patient outcomes.