Automated assessment of mandibular shape asymmetry in 3-dimensions

Introduction: The purpose of this study was to develop an automatic pipeline for analyzing mandibular shape asymmetry in three dimensions (3D). Methods: 40 skeletal Class I patients and 80 skeletal Class III patients were used. The mandible was automatically segmented from the CBCT images using a U-net deep learning network. 17,415 uniformly sampled quasi-landmarks were automatically identified on the mandibular surface via a template mapping technique. After alignment with the robust Procrustes superimposition, the pointwise surface-to-surface distance between original and reflected mandibles was visualized in a color-coded map, indicating where the asymmetry occurs. The degree of overall mandibular asymmetry and the asymmetry of sub-skeletal units were scored using the root-mean-squared-error between the left and right sides. These asymmetry parameters were compared between the skeletal Class I and skeletal Class III groups. Results: The shape of the mandible was significantly more asymmetrical in skeletal Class III patients with positional asymmetry. The condyles were identified as the most asymmetric region in all groups, followed by the coronoid process and the ramus. Conclusions: This automated approach to quantify mandibular shape asymmetry will facilitate high-throughput image processing for big data analysis. The spatially dense landmarks allow for evaluating mandibular asymmetry over the entire surface, which overcomes the information loss inherent in conventional linear distance or angular measurements. Precise quantification of the asymmetry can provide important information for individualized diagnosis and treatment planning in orthodontics and orthognathic surgery.

Publication year:2022

Accessibility:Open