• Effectiveness of automated seg...

*Result*: Effectiveness of automated segmentation of maxillofacial structures in cone-beam computed tomography images using artificial intelligence: A systematic review.

Title:
Effectiveness of automated segmentation of maxillofacial structures in cone-beam computed tomography images using artificial intelligence: A systematic review.
Authors:
Iacob AM; Orthodontics Division, Asturian Dentistry Institute, University of Oviedo, Street Catedrático Jose Serrano 10, 33006 Oviedo, Spain. Electronic address: alini63@yahoo.com., Verdecchia A; Orthodontics Division, Asturian Dentistry Institute, University of Oviedo, Street Catedrático Jose Serrano 10, 33006 Oviedo, Spain; Department of Surgical Sciences, Postgraduate School in Orthodontics, University of Cagliari, 09124 Cagliari, Italy., García-Mesa Y; Grupo SINPOS, Department of Cell Biology and Morphology, University of Oviedo, 33006 Oviedo, Spain; Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain., Spinas E; Department of Surgical Sciences, Postgraduate School in Orthodontics, University of Cagliari, 09124 Cagliari, Italy., Cobo T; Orthodontics Division, Asturian Dentistry Institute, University of Oviedo, Street Catedrático Jose Serrano 10, 33006 Oviedo, Spain.
Source:
International orthodontics [Int Orthod] 2026 Mar; Vol. 24 (1), pp. 101081. Date of Electronic Publication: 2025 Oct 08.
Publication Type:
Journal Article; Systematic Review; Review
Language:
English
Journal Info:
Publisher: Masson Country of Publication: France NLM ID: 101184882 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-680X (Electronic) Linking ISSN: 17617727 NLM ISO Abbreviation: Int Orthod Subsets: MEDLINE
Imprint Name(s):
Original Publication: Paris : Masson, c2003-
MeSH Terms:
Cone-Beam Computed Tomography*/methods , Maxilla*/diagnostic imaging , Mandible*/diagnostic imaging , Image Processing, Computer-Assisted*/methods , Artificial Intelligence*, Humans ; Neural Networks, Computer
Contributed Indexing:
Keywords: Artificial intelligence; Cone-beam computed; Convolutional neural network; Dentistry; Image processing; Jawbone; Orthodontics; Segmentation; Tomography
Entry Date(s):
Date Created: 20251009 Date Completed: 20260221 Latest Revision: 20260221
Update Code:
20260222
DOI:
10.1016/j.ortho.2025.101081
PMID:
41066968
Database:
MEDLINE

*Further Information*

*Background: The automated segmentation of maxillary and mandibular bones in cone-beam computed tomography (CBCT) using artificial intelligence (AI) is redefining the standards of digital dentistry and orthodontics, with applications in mini-implant placement, dental implantology, orthognathic surgery, and bone graft planning.
Objective: To systematically assess the performance of AI models - particularly U-Net-based convolutional neural networks (CNNs) - for automated segmentation of maxillary bone structures in CBCT, following the PICOS model (Population - CBCT scans of human maxillae; Intervention - AI-based segmentation; Comparator - manual segmentation; Outcome - accuracy; Study design - diagnostic accuracy studies).
Material and Methods: This systematic review adhered to PRISMA 2020 guidelines and was registered in PROSPERO (CRD42024592182). Eligibility criteria included studies applying AI to maxillary bone segmentation in CBCT and reporting quantitative accuracy metrics. Risk of bias was evaluated using the QUADAS-2 tool. The GRADE tool for formulating and grading recommendations in clinical practice was also employed. Data collected comprised number of CBCT scans, AI model architecture, evaluation metrics, and reported clinical applications.
Results: Thirty-one studies, analysing 11,432 CBCT scans, met the inclusion criteria. AI models consistently achieved high segmentation accuracy, with Dice similarity coefficients frequently exceeding 0.98, while substantially reducing processing time compared to manual segmentation. Applications ranged from implant planning and orthognathic surgery to digital orthodontics. Persistent challenges included anatomical variability, imaging artifacts, and the limited availability of high-quality annotated datasets.
Conclusions: AI-based segmentation of maxillary and mandibular bones in CBCT demonstrates promising accuracy and efficiency compared with manual techniques. Nevertheless, the certainty of evidence is limited by retrospective designs and small, heterogeneous samples. Large-scale, prospective multicentre studies with standardized evaluation are needed before these methods can be reliably adopted in routine clinical practice.
(Copyright © 2025 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)*

*Disclosure of interest The authors declare that they have no competing interest.*