Accuracy of a deep learning-based model for treatment recommendation in adult patients with skeletal Class III malocclusion.

INTRODUCTION

Treatment planning for adult patients with skeletal Class III malocclusion remains challenging because of overlapping diagnostic criteria and subjective weighting of skeletal vs soft-tissue considerations. This retrospective study aimed to develop and evaluate the accuracy of a convolutional neural network (CNN)-based image classification in predicting treatment approach and supporting orthodontists in deciding between orthodontic camouflage and orthognathic surgery.

METHODS

Using 1826 pretreatment images of 166 adult patients with skeletal Class III malocclusion (86 camouflage and 80 surgical), a hybrid model was developed that combines both deep learning and machine learning. These images included lateral cephalometric and panoramic radiographs and 9 intraoral and extraoral photographs. Of note, 11 CNN models processed each image type to generate binary predictions that were combined into an 11-dimensional vector and classified using 7 conventional machine learning algorithms.

RESULTS

Support vector machine, multilayer perceptron, logistic regression, k-nearest neighbor, and naive Bayes showed no statistically significant difference compared with random forest (P >0.05). Decision tree exhibited statistically significant inferior performance compared with random forest (P <0.01). Significance analysis indicated that soft-tissue photographs had a higher correlation with treatment decisions than that of cephalometric radiographs, although clinical validity requires expert confirmation.

CONCLUSIONS

A CNN-based ensemble model demonstrated high diagnostic accuracy for predicting camouflage vs surgical treatment in adult patients with skeletal Class III malocclusion within a single-center dataset.