Development of Artificial Intelligence-Supported Automatic Three-Dimensional Surface Cephalometry

Tanikawa, Chihiro; Nakamura, Hiroyuki; Mimura, Takaaki et al.

Orthodontics and Craniofacial Research, 2025

Number of Access:648（2025-08-21 07:02 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/100959

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Title
Development of Artificial Intelligence-Supported Automatic Three-Dimensional Surface Cephalometry
Creator
NRID
1000070423142 1000070423142
ORCID
0000-0003-3390-2900 0000-0003-3390-2900
researchmap permalink
read0091612 read0091612
Scopus Author ID
22837117000 22837117000
CreatorName
Tanikawa, Chihiro
CreatorName
Nakamura, Hiroyuki
CreatorName
Mimura, Takaaki
NRID
1000090848251 1000090848251
CreatorName
Uemura, Yume
NRID
1000070294428 1000070294428
ORCID
0000-0002-4419-9643 0000-0002-4419-9643
researchmap permalink
read0051793 read0051793
Scopus Author ID
55416765600 55416765600
CreatorName
Yamashiro, Takashi
Subject
artificial intelligence
cephalometry
humans
spiral cone-beam computed tomography
Description
Tanikawa C., Nakamura H., Mimura T., et al. Development of Artificial Intelligence-Supported Automatic Three-Dimensional Surface Cephalometry. Orthodontics and Craniofacial Research , (2025); https://doi.org/10.1111/ocr.12914.
Abstract
Objective: Surface-based three-dimensional (3D) cephalometry provides detailed clinical information for the analysis of craniofacial structures. This study aimed to develop an automated 3D surface cephalometry system using mesh fitting based on landmarks identified by artificial intelligence (AI) and to evaluate its accuracy. Methods: A total of 185 CBCT images from adult Japanese patients (system training, n = 152; evaluation, n = 33) were used in this study. Cranial and mandibular images were generated via surface rendering of CBCT images. An experienced orthodontist manually recognised 19 and 45 3D landmarks for the cranium and mandible, respectively, and used them as the gold standard after they were checked by another experienced orthodontist. An AI system developed using PointNet ++ was trained to output landmark coordinates based on surface data and normal vectors. Mesh fitting (homologous modelling) was then conducted using the AI-identified landmarks. The errors in mesh fitting were evaluated. Results: The mean errors for wire mesh fittings with AI-identified landmarks for the maxilla and mandible were 0.80 ± 0.57 mm and 1.45 ± 0.34 mm, respectively. Discussion: An AI-based landmark identification system and mesh fittings that demonstrate clinically acceptable accuracy were presented. This system can be applied in clinical settings to quantify and visualise craniofacial structures in three dimensions. Conclusion: The automated 3D surface cephalometry system utilising mesh fitting based on AI-identified landmarks showed clinically acceptable accuracy. This allows orthodontists to compare a patient's craniofacial surface with normative data, without the need for manual landmark identification.
Publisher
John Wiley and Sons Inc
Source Title
Orthodontics and Craniofacial Research
Date of Issued
2025-03-04
Language
English
Handle URL
https://hdl.handle.net/11094/100959
PISSN
16016335
eISSN
16016343
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1111/ocr.12914
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article