Automated artificial intelligence-driven 3-dimensional craniofacial superimposition for clinically useful treatment outcomes in growing patients: A multicenter study.

INTRODUCTION

In growing patients, reliable quantification of change requires explicitly stating the reference used for superimposition and interpreting all values as relative changes; however, manual workflows are time-consuming and variable. This study assessed measurement reliability and workflow efficiency for reference-explicit analyses, comparing a fully automated, open-source segmentation and registration workflow against a semiautomatic voxel-based approach for clinically useful 3-dimensional assessments.

METHODS

Twenty-two Class II patients with cone-beam computed tomographies at pretreatment (T1) and posttreatment (T2) were analyzed. Automated segmentation and voxel-based superimposition were performed with built-in quantitative analysis. Primary outcomes were skeletal and dental changes relative to cranial base and regional maxilla and mandibular superimposition. Three registration approaches incorporating varying levels of artificial intelligence (AI) involvement-conventional, semiautomated, and fully automated-were compared. Performances were assessed by mixed-effects linear regression models.

RESULTS

Agreement for skeletal and dental measurements was high, with minor differences observed between AI-driven and conventional registration approaches, and all methods showed clinically comparable precision. Most absolute average differences between automated and conventional workflows are under 1.5 mm for linear and 1.5° for angular measurements. Cranial base superimposition differences showed an average difference in T2-T1 changes ranging from -0.3 to 0.7 mm, whereas regional superimposition differences showed an average difference in T2-T1 changes ranging from -1.7 to 1.1 mm.

CONCLUSIONS

Automated clinician-verified workflow yields reliable and faster 3-dimensional change measures in growing patients. Interpretation must consider the reference region used for superimposition. AI-driven open-source tools provide a practical quantitative analysis to support diagnosis, timing, and assessment of treatment outcomes.