Predicting anterior cruciate ligament failure load with T₂* relaxometry and machine learning as a prospective imaging biomarker for revision surgery.

ABSTRACT: Non-invasive methods to document healing anterior cruciate ligament (ACL) structural properties could potentially identify patients at risk for revision surgery. The objective was to evaluate machine learning models to predict ACL failure load from magnetic resonance images (MRI) and to determine if those predictions were related to revision surgery incidence. It was hypothesized that the optimal model would demonstrate a lower mean absolute error (MAE) than the benchmark linear regression model, and that patients with a lower estimated failure load would have higher revision incidence 2 years post-surgery. Support vector machine, random forest, AdaBoost, XGBoost, and linear regression models were trained using MRI T₂* relaxometry and ACL tensile testing data from minipigs (n = 

SUBMITTER: Flannery SW 

PROVIDER: S-EPMC9981601 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature