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Explainable machine learning identifies knee morphology thresholds for arthroscopic medial meniscus posterior root
Minyuan Zhang1, Fengyuan Guo1, Yanlin Li1
1Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Background:
Medial meniscus posterior root tear (MMPRT) is clinically important because disruption of posterior root function compromises hoop tension and load sharing, accelerating medial-compartment degeneration. Early or subtle posterior pathology may be underrecognized on MRI, highlighting the potential value of morphology-based risk awareness during MRI interpretation.
Methods:
We retrospectively analyzed 608 patients who underwent arthroscopic surgery for knee joint injuries, including 281 patients with arthroscopically confirmed MMPRT and 327 controls without MMPRT. Demographic, clinical, and MRI-based morphologic parameters were compared in the training set. Variables identified from training-set comparisons and clinical/biomechanical relevance were used to develop 10 machine learning models, including CatBoost, Decision Tree, GBM, LightGBM, LASSO, Naive Bayes, Neural Network, Random Forest, Support Vector Machine, and XGBoost. Models were evaluated with 10-fold cross-validation and an independent testing set. Explainability was assessed using SHapley Additive exPlanations (SHAP), including global importance and dependence plots.
Results:
Training-set multivariable analysis identified older age, greater medial tibial slope (MTS), and deeper medial tibial plateau depth (MTPD) as independent factors associated with MMPRT. GBM achieved the highest AUC in the independent testing set and was selected for SHAP-based interpretation. SHAP analysis ranked age as the dominant contributor, followed by MTS and MTPD. Dependence plots suggested non-linear, threshold-like patterns in model contribution. Age showed an apparent transition from negative to positive SHAP contributions around midlife, MTS showed a threshold-like increase between approximately 6° and 8°, whereas MTPD shifted toward positive SHAP contributions around approximately 2.2-2.5 mm.
Conclusion:
Age was the dominant model contributor, and MTS and MTPD were independently associated with MMPRT, exhibiting non-linear patterns in SHAP-based model interpretation. These findings may help raise suspicion for MMPRT in symptomatic patients undergoing MRI, particularly when age-related and tibial plateau morphologic risk patterns are present.
Insights
Medial meniscus posterior root tears (MMPRT) are linked to older age, increased medial tibial slope (MTS), and deeper medial tibial plateau depth (MTPD). These factors, identified through machine learning, can help radiologists suspect MMPRT on MRI.
Area of Science:
- Orthopedics
- Radiology
- Biomedical Engineering
Background:
- Medial meniscus posterior root tears (MMPRT) compromise knee joint function and accelerate degeneration.
- Subtle MMPRT may be missed on MRI, necessitating morphology-based risk assessment.
- Understanding risk factors improves early diagnosis and management of MMPRT.
Purpose of the Study:
- To identify demographic and morphologic predictors of MMPRT using machine learning.
- To develop and validate predictive models for MMPRT detection on MRI.
- To enhance MRI interpretation for early identification of MMPRT.
Main Methods:
- Retrospective analysis of 608 knee arthroscopy patients (281 MMPRT, 327 controls).
- Development of 10 machine learning models using demographic, clinical, and MRI morphologic data.
- Model evaluation via cross-validation and independent testing, with SHAP for explainability.
Main Results:
- Older age, greater medial tibial slope (MTS), and deeper medial tibial plateau depth (MTPD) were independent predictors of MMPRT.
- The GBM model showed highest accuracy, with age as the dominant factor, followed by MTS and MTPD.
- SHAP analysis revealed non-linear, threshold-like contributions for age, MTS, and MTPD.
Conclusions:
- Age, MTS, and MTPD are key independent factors associated with MMPRT.
- Non-linear patterns in these factors influence MMPRT risk.
- These findings aid in raising suspicion for MMPRT in symptomatic patients with specific MRI findings.
