Machine Learning-Assisted Classification of Hip Conditions in Pediatric Cerebral Palsy Patients Using Migration Percentage Measurements

Abstract

Hip displacement is a significant concern in children with cerebral palsy (CP), necessitating accurate and timely assessment to prevent long-term complications. This study developed a support vector machine (SVM) model to classify hip conditions using migration percentage (MP) measurements obtained from 176 hips across 88 anteroposterior pelvic radiographs. MP values were categorized into three groups: normal (MP ≤ 30 %), risky (30 % < MP ≤ 60 %), and dislocated (MP > 60 %). The SVM model was evaluated using stratified k-fold crossvalidation, with accuracy, precision, recall, and F1-scores as key metrics. Its classifications were compared to manual evaluations performed by an orthopedic resident and a pediatric orthopedic surgeon. The model achieved an overall accuracy of 92.898 %, surpassing the consistency and reliability of manual assessments, particularly in identifying dislocated hips. Statistical analysis showed no significant differences between the model's MP measurements and those of the clinicians, validating its effectiveness. This study highlights the potential of SVM models to enhance diagnostic accuracy, reduce variability in evaluations, and support clinical decision-making. Future research should expand the dataset and incorporate advanced machine learning models to further improve diagnostic precision.