A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence

Abstract

Background: Supervised machine learning models in artificial intelligence (AI) have been increasingly used to predict different types of events. However, their use in orthopaedic surgery has been limited. Hypothesis: It was hypothesized that supervised learning techniques could be used to build a mathematical model to predict primary anterior cruciate ligament (ACL) injuries using a set of morphological features of the knee. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Included were 50 adults who had undergone primary ACL reconstruction between 2008 and 2015. All patients were between 18 and 40 years of age at the time of surgery. Patients with a previous ACL injury, multiligament knee injury, previous ACL reconstruction, history of ACL revision surgery, complete meniscectomy, infection, missing data, and associated fracture were excluded. We also identified 50 sex-matched controls who had not sustained an ACL injury. For all participants, we used the preoperative magnetic resonance images to measure the anteroposterior lengths of the medial and lateral tibial plateaus as well as the lateral and medial bone slope (LBS and MBS), lateral and medial meniscal height (LMH and MMH), and lateral and medial meniscal slope (LMS and MMS). The AI predictor was created using Matlab R2019b. A Gaussian naïve Bayes model was selected to create the predictor. Results: Patients in the ACL injury group had a significantly increased posterior LBS (7.0° ± 4.7° vs 3.9° ± 5.4°; P = .008) and LMS (–1.7° ± 4.8° vs –4.0° ± 4.2°; P = .002) and a lower MMH (5.5 ± 0.1 vs 6.1 ± 0.1 mm; P = .006) and LMH (6.9 ± 0.1 vs 7.6 ± 0.1 mm; P = .001). The AI model selected LBS and MBS as the best possible predictive combination, achieving 70% validation accuracy and 92% testing accuracy. Conclusion: A prediction model for primary ACL injury, created using machine learning techniques, achieved a >90% testing accuracy. Compared with patients who did not sustain an ACL injury, patients with torn ACLs had an increased posterior LBS and LMS and a lower MMH and LMH.

Effective automatic detection of anterior cruciate ligament injury using convolutional neural network with two attention mechanism modules

“…In the continuous updating of the CNN model, a CNN can also achieve better accuracy and sensitivity. Moreover, the input data of the model are no longer limited to similar data sets, and the classification criteria are no longer divided into whether the ACL is injured, or uninjured, so we must turn to the prediction of ACL injuries [ 28 ] and ACL reconstruction failures. However, the process of transforming clinical trial result and applying this technology in a such a way that helps clinicians remains stagnant.…”

The Principles of Knee Joint Preservation: Operative Treatment Strategies

“…Christensen et al 17 found a mean posterior tibial slope of 8.4° among patients in whom the ACL reconstruction failed within 2 years of the surgical procedure, compared with 6.5° among patients with no evidence of graft failure. An elevated posterior tibial slope has also been identified in patients with primary ACL injury in comparison with sex-matched controls with no history of ACL injury 22 .…”

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

“…Tamini et al employed supervised ML models to construct a predictive mathematical model for primary ACL injuries, using a set of knee morphological characteristics [21]. Preoperative MRI scans were utilized to measure the anteroposterior lengths of the medial and lateral tibial plateaus, as well as the lateral and medial bone slope, lateral and medial meniscal slope, and lateral and medial menisci.…”