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Updated: Jan 15, 2026
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
Published on: February 23, 2024
Effectiveness of automated segmentation of maxillofacial structures in cone-beam computed tomography images using
Alin M Iacob1, Alessio Verdecchia2, Yolanda García-Mesa3
1Orthodontics Division, Asturian Dentistry Institute, University of Oviedo, Street Catedrático Jose Serrano 10, 33006 Oviedo, Spain.
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.
