Geometric morphometrics based diagnostic model for Skeletal Class III patients.

BACKGROUND

Skeletal Class III (SCIII) malocclusion represents a heterogeneous cluster of craniofacial anomalies characterised by a sagittal mesial discrepancy. It is among the most challenging orthodontic conditions to treat because there is no standardisation regarding subphenotype classification or treatment efficacy prediction. This study aimed to develop a data-driven model to identify novel clinically relevant SCIII subphenotypes, contributing to tailored treatment protocols.

METHODS

A clinical subphenotypic classification model for SCIII was developed using 12 annotated craniofacial landmarks from lateral cephalometric radiographs of 655 adult SCIII patients of white origin. SCIII subphenotypes were identified by applying generalised Procrustes analysis and unsupervised clustering, and a classification model was developed for predicting subphenotypes for new patients. Cross-validation was employed to demonstrate the robustness of our clustering and classification models.

RESULTS

Here we show that our model inferred six distinct subphenotypes that unravelled relevant morphological features in SCIII patients. We further demonstrate the generalisability of our model across ethnicities using an external validation cohort of patients of Korean origin. The identified SCIII subphenotypes exhibit a strong correlation with treatment decision.

CONCLUSIONS

Our results contribute to the development of an accurate SCIII diagnostic tool (available at https://tools.istars.pt/sciii/ ), moving towards the goal of improving treatment efficacy for this condition.