@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}

}

@article{nokey,

title = {A semi-automated assessment tool for craniofacial landmarks in CBCT: InVivo7 software},

author = {Luísa Schubach da Costa Barreto and Bruno Moreira das Neves and Jonas Bianchi and Heesoo Oh and Klaus Barretto dos Santos Lopes Batista and Jose Augusto Mendes Miguel},

url = {https://www.sciencedirect.com/science/article/pii/S0300571225007353?pes=vor&utm_source=scopus&getft_integrator=scopus},

doi = {https://doi.org/10.1016/j.jdent.2025.106292},

year  = {2026},

date = {2026-02-01},

urldate = {2026-02-01},

journal = {Journal of Dentistry},

volume = {165},

abstract = {Objectives: This study describes and evaluates the functionality of the InVivo7 3D imaging software as a semiautomated tool for identifying craniofacial landmarks in CBCT scans. Methods: AI-assisted landmark tracing in InVivo7 was used to automatically identify anatomical points in CBCT images. Each landmark was manually verified by a skilled evaluator to ensure accurate and reliable results, particularly for soft tissue markers and dental measurements, which often presented challenges for AI detection. The study utilized a standardized cephalometric analysis to compare the software’s performance. The evaluation included assessing the software’s ability to recognize skeletal, dental, and soft tissue structures accurately. Results: The semi-automated AI-assisted algorithm showed high precision in landmark identification. Manual verification confirmed its reliability and allowed the creation of a customized automated configuration for orthodontic diagnosis and treatment outcome evaluation. Specific clinical measures, such as the facial plane angle and molar relationships, were calculated using established formulas, allowing the software to categorize molar relationship classes (Angle Class I, II, III). Conclusions: InVivo7 presents a reliable and efficient tool for craniofacial landmark analysis, enhancing diagnostic accuracy while reducing manual labor. However, ongoing validation and software updates are essential to fully optimize its clinical applicability and ensure consistent performance across diverse patient populations. Future developments should focus on refining AI algorithms to improve soft tissue landmark detection and expanding datasets to enhance the robustness of automated analyses. Clinical Relevance: Rule-based automated algorithm CBCT craniofacial landmark detection using InVivo7 provides accurate, reproducible measurements, reducing manual workload and enhancing orthodontic diagnostic efficiency. Its integration into clinical practice supports standardized assessments, streamlining treatment planning.},

keywords = {3d, anatomic landmarks, artificial intelligence, CBCT, orthodontics},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2022b,

title = {Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans during 3D UNETR},

author = {Gillot M and Baquero B and Le C and R Deleat-Besson and Bianchi J and Gurgel M and Yatabe M and Al Turkestani N and Najarian K},

url = {https://pubmed.ncbi.nlm.nih.gov/36223330/},

doi = {10.1371/journal.pone.0275033},

year  = {2022},

date = {2022-10-12},

journal = {PLoS One},

volume = {17},

issue = {10},

abstract = {The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.},

keywords = {3d, CBCT, Cone-beam computed tomography (CBCT), multi-anatomical skull structure, structure segmentation},

pubstate = {published},

tppubtype = {article}

}