Role of artificial intelligence in treatment planning and outcome prediction of jaw corrective surgeries by using 3-D imaging-a systematic review.

Objective: Artificial intelligence (AI) has been increasingly utilized in diagnosis of skeletal deformities, while its role in treatment planning and outcome prediction of jaw corrective surgeries with 3-dimensional (3D) imaging remains underexplored.

Methods: The comprehensive search was done in PubMed, Google scholar, Semantic scholar and Cochrane Library between January 2000 and May 2024. Inclusion criteria encompassed studies on AI applications in treatment planning and outcome prediction for jaw corrective surgeries using 3D imaging. Data extracted included study details, AI algorithms, and performance metrics. Modified PROBAST tool was used to assess the risk of bias (ROB).

Results: Fourteen studies were included. 11 studies used deep learning algorithms, and 3 employed machine learning on CT data. In treatment planning the prediction error was 0.292 to 3.32 mm (N = 5), and Dice score was 92.24 to 96% (N = 2). Accuracy of outcome predictions varied from 85.7% to 99.98% (N = 2). ROB was low in most of the included studies. A meta-analysis was not conducted due to significant heterogeneity and insufficient data reporting in the included studies.

Conclusion: 3D imaging-based AI models in treatment planning and outcome prediction for jaw corrective surgeries show promise but remain at proof-of-concept. Further, prospective multicentric studies are needed to validate these findings.