2025 BioMed research international

Application of Artificial Intelligence in Orthognathic Surgery: A Scoping Review.

, , , , , ,

BioMed research international Vol. 2025 : 8284581 • Jan 2025

Objective: This study was aimed at reviewing the application of different artificial intelligent algorithms used in different phases of orthognathic surgeries, which include diagnosis, treatment planning, soft tissue prediction, outcome evaluation, and complication assessment. This aimed to update clinicians on this technology to integrate it into their decision-making, in addition to being aware of its challenges and potential areas for further assessment. Materials and Methods: Electronic search was done in PubMed, Scopus, Embase, and Cochrane databases. Studies that reported the application of artificial intelligence (AI) in different aspects of orthognathic surgery were included. Results: From 656 studies, a total of 29 articles met the inclusion criteria and were used to categorize the application of AI as follows: (1) Diagnosis in which studies showed the sensitivity of 75%-95.5% for specifying the need for orthognathic surgery; (2) treatment planning in which AI was used for osteotomy design and bony reference point determination with 3.99-4.73 mm of error; (3) soft tissue prediction in which AI models showed a success rate of 64.3%-100%; (4) outcome evaluation in which AI was used to assess the impact of surgery on asymmetry, facial attractiveness, and esthetic improvements quantitatively; and (5) complication assessment with an accuracy of 98.7% for predicting postsurgical systemic infection and 7.4 mL of error for blood loss. Conclusion: AI can be potentially considered as a proper alternative to conventional approaches to fasten the procedure related to orthognathic surgery and with comparable accuracy to conventional methods.

No clinical trial protocols linked to this paper

Clinical trials are automatically linked when NCT numbers are found in the paper's title or abstract.
PICO Elements

No PICO elements extracted yet. Click "Extract PICO" to analyze this paper.

Paper Details
MeSH Terms
Associated Data

No associated datasets or code repositories found for this paper.

Related Papers

Related paper suggestions will be available in future updates.