@article{nokey,

title = {Automated artificial intelligence driven 3-dimensional craniofacial superimposition for clinically useful treatment outcomes in growing patients: A multicenter study},

author = {Yanjie Huang and Luc Anchling and William Ray Bates and Maxime Gillot and Juan Carlos Prieto and Tung Nguyen and Selene Barone and Felicia Miranda and Marcela Gurgel and Marilia Yatabe and Karine Evangelista and Brent E. Larson and Jonas Bianchi and Antonio Carlos de Oliveira Ruellas and Lucia Cevidanes},

doi = {https://doi.org/10.1016/j.ajodo.2025.12.013},

issn = {0889-5406},

year  = {2026},

date = {2026-06-14},

journal = {American Journal of Orthodontics and Dentofacial Orthopedics},

volume = {169},

issue = {6},

pages = {753-765},

abstract = {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.},

keywords = {3d, artificial intelligence, craniofacial superimposition, growing patients, multicenter study},

pubstate = {published},

tppubtype = {article}

}