Automated Classification of Skeletal Facial Asymmetry in CBCT Using a Reproducible 3D Slicer Workflow for Patient-Specific Decision Support.

This study presents an automated pipeline for classifying skeletal facial asymmetry of 170 patients with Class III malocclusion using cone-beam computed tomography (CBCT). The methodology integrates standardized image preprocessing, mirroring, voxel-based registration, anatomical measurement extraction, dimensionality reduction, and clustering. Principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) were evaluated using Spectral Clustering with both k-nearest-neighbors (kNN) and radial basis function (RBF) affinities. PCA suggested four clusters, t-SNE found four with kNN and two with RBF, and UMAP identified six. UMAP with RBF affinity achieved the best overall clustering performance, as indicated by the silhouette score, C-index, Dunn index, and closed surface distance heat maps, slightly outperforming UMAP with kNN. The unsupervised framework established in this study provides clinically meaningful phenotypes that could inform subsequent supervised classification work with an expanded dataset. The pipeline is implemented within open-source 3D Slicer Craniomaxillofacial automated modules to enhance reproducibility and clinical translation.