Artificial intelligence-driven soft tissue prediction in virtual surgical planning for orthognathic surgery: a scoping review.
Authors
Affiliations (4)
Affiliations (4)
- Private Practice, Hobart, Tasmania, Australia. Electronic address: [email protected].
- Private Practice, Melbourne, Victoria, Australia.
- Department of Rural Clinical Sciences, La Trobe Rural Health School, Bendigo, Victoria, Australia.
- Department of Oral and Maxillofacial Surgery, The Royal Melbourne Hospital, Melbourne, Victoria, Australia.
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.