Abstract
BACKGROUND: Magnetic resonance imaging (MRI) assessments are considered important for predicting patellar dislocation. Risk factors for patellar dislocation have not yet been clearly identified. PURPOSE: To identify patellofemoral instability anatomic risk factors using MRI and enhance the area under the curve (AUC) through optimized machine learning (ML) methods. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: An age- and sex-matched control group of 121 patients was selected. The assessed patellofemoral morphological parameters included trochlear depth, sulcus angle, trochlear facet asymmetry, lateral trochlear inclination, Wiberg index, lateral patellar facet angle, Wiberg angle, patellar tilt, and trochlear medialization. Differences between groups were analyzed based on the MRI parameters. In addition, ML models were created and optimized using these measured parameters to investigate their diagnostic potential. Logistic regression analysis (LRA), support vector machine (SVM), and light gradient boosting machine (LGBM) were employed as machine learning techniques. RESULTS: Sex differences were found in trochlear depth, trochlear groove medialization, lateral patellar facet angle, and Wiberg angle, whereas no sex differences were observed in the other measured parameters. Significant differences between the control and dislocation groups were found for all patellofemoral morphological parameters, except for trochlear facet asymmetry. A significant difference was observed in the patellar height parameter between the 2 groups. Among the measured patellofemoral parameters, patellar tilt showed the highest AUC (0.8). Of the optimized ML models, the LGBM demonstrated the highest AUC at 0.873. When the number of variables used in the SVM model, which employed the fewest variables, was applied to both LGBM and LRA, the SVM model achieved the highest AUC at 0.858. CONCLUSION: Patellar tilt and trochlear depth exhibited the strongest correlation with patellar dislocation. The optimized ML techniques showed improved AUC values compared with those of existing models. However, while a higher AUC can be achieved by using more variables, this approach has proven to be inefficient. Therefore, for practical clinical applications, it is important to focus on using the minimum number of variables in optimized ML models.