Authors
Daniel Frank, Zahraa Albarki, Mohamed Naqeeb, Ricky Kumar
Published in
The British journal of oral & maxillofacial surgery. Sep 14, 2026. Epub Sep 14, 2026.
Abstract
The objective of this study was to map the current evidence on artificial intelligence (AI)-based facial soft tissue prediction in virtual surgical planning (VSP) workflows for orthognathic surgery. A scoping review was conducted using PubMed/MEDLINE, Embase, Scopus, Web of Science and IEEE Xplore databases, in accordance with the Joanna Briggs Institute (JBI) methodology and reported following the PRISMA Extension for Scoping Reviews (PRISMA-ScR). Data on study characteristics, AI methodologies, imaging inputs, accuracy metrics, validation strategies, and levels of integration into VSP were extracted. Six studies met the eligibility criteria. All used deep learning-based models, predominantly using point cloud or surface mesh representations derived from computed tomography (CT), cone-beam computed tomography (CBCT), and/or three-dimensional (3D) surface scans. The reported geometric prediction accuracy was frequently within clinically accepted thresholds (≤ 2-3 mm), although reduced accuracy was consistently observed in dynamic regions. None of the studies adequately evaluated patient-reported outcomes, clinician usability, or full workflow integration with VSP. AI-based soft tissue prediction in VSP workflows demonstrates promising results but remains in an early translational phase. Further research is required to establish its generalisability, workflow integration, and clinical impact.
PMID:
42971846
Bibliographic data and abstract were imported from PubMed on 03 Nov 2026.
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