The Clinical Application of Machine Learning Models for Risk Analysis of Ramp Lesions in Anterior Cruciate Ligament Injuries

Background: Peripheral tears of the posterior horn of the medial meniscus, known as “ramp lesions,” are commonly found in anterior cruciate ligament (ACL)–deficient knees but are frequently missed on routine evaluation. Purpose: To predict the presence of ramp lesions in ACL-deficient knees using machine learning methods with associated risk factors. Study Design: Cohort study (Diagnosis); Level of evidence, 2. Methods: This study included 362 patients who underwent ACL reconstruction between June 2010 and March 2019. The exclusion criteria were combined fractures and multiple ligament injuries, except for medial collateral ligament injuries. Patients were grouped according to the presence of ramp lesions on arthroscopic surgery. Binary logistic regression was used to analyze risk factors including age, sex, body mass index, time from injury to surgery (≥3 or Results: Ramp lesions were identified in 112 patients (30.9%). The risk for ramp lesions increased with steeper medial tibial and meniscal slopes, higher knee laxity, and an increased LFC ratio. Comparing the final performance of all models, the random forest model yielded the best performance (area under the curve: 0.944), although there were no significant differences among the models ( P > .05). The cut-off values for the presence of ramp lesions on receiver operating characteristic analysis were as follows: medial tibial slope >5.5° ( P < .001), medial meniscal slope >5.0° ( P < .001), and LFC ratio >71.3% ( P = .033). Conclusion: Steep medial tibial and meniscal slopes, an increased LFC ratio, and higher knee rotatory laxity were observed risk factors for ramp lesions in patients with an ACL injury. The prediction model of this study could be used as a supplementary diagnostic tool for ramp lesions in ACL-injured knees. In general, care should be taken in patients with ramp lesions and its risk factors during ACL reconstruction.