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A Mouse Model of Ankle-Subtalar Complex Joint Instability

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Comparative Analysis of 3 Machine Learning Methods for Identifying Minimal Predictors of Patellofemoral Instability

Hyuck Min Kwon¹, Ji-Hoon Nam^2,3, Yong-Gon Koh⁴

¹Joint Department of Orthopaedic Surgery, Yonsei University College of Medicine, Severance Hospital, Seoul, Republic of Korea.

Orthopaedic Journal of Sports Medicine

|July 30, 2025

Summary

This summary is machine-generated.

Magnetic resonance imaging (MRI) can predict patellar dislocation by analyzing anatomical risk factors. Optimized machine learning (ML) models, particularly those using fewer variables, show improved diagnostic potential for patellofemoral instability.

Keywords:

light gradient boosting machine logistic regression analysis machine learning patellofemoral instability support vector machine

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Area of Science:

Orthopedics and Sports Medicine
Radiology
Biomedical Engineering

Background:

Patellar dislocation prediction often relies on Magnetic Resonance Imaging (MRI) assessments.
Identifying specific anatomical risk factors for patellar dislocation remains a challenge.

Purpose of the Study:

To identify anatomical risk factors for patellofemoral instability using MRI.
To enhance diagnostic accuracy through optimized machine learning (ML) models and improved Area Under the Curve (AUC).

Main Methods:

A case-control study involving 121 age- and sex-matched patients.
Assessment of patellofemoral morphological parameters including trochlear depth, sulcus angle, and patellar tilt.
Development and optimization of ML models (Logistic Regression Analysis, Support Vector Machine, Light Gradient Boosting Machine) to analyze diagnostic potential.

Main Results:

Significant differences in patellofemoral parameters were found between control and dislocation groups, except for trochlear facet asymmetry.
Patellar tilt demonstrated the highest individual AUC (0.8).
Optimized ML models, especially Light Gradient Boosting Machine (LGBM), achieved higher AUCs (up to 0.873), with Support Vector Machine (SVM) reaching 0.858 using minimal variables.

Conclusions:

Patellar tilt and trochlear depth are key indicators for patellar dislocation.
Optimized ML models significantly improve diagnostic AUC compared to existing methods.
For clinical utility, focusing on optimized ML models with a minimal set of variables is crucial for efficient patellofemoral instability assessment.