Traditional and Artificial Intelligent Methods in Predicting Maxillofacial Soft Tissue Morphology After Orthognathic Surgery: A Narrative Review.

Abstract

The soft tissue morphology after orthognathic surgery has always been difficult to predict in preoperative planning. This narrative review aims to evaluate the current prediction methods and point out the direction for future research to improve predicting accuracy and speed. PubMed was the database for the review. The keywords for searching include: orthognathic surgery, prediction methods, simulation methods, artificial intelligence (AI), machine learning (ML), deep learning (DL), soft tissue, and surgical planning. Sixty-seven articles were found and 60 relevant articles were evaluated. The review finds out that with the transition from two-dimensional (2D) prediction to three-dimensional (3D) prediction, AI algorithms, especially DL methods, are brought forward promisingly, such as deep neural network (DNN), PointNet, and Transformer. DL methods have the advantages of high accuracy, fast speed, and simplified use, therefore have broad development prospects. However, DL methods need to be further improved, mainly in data insufficiency, overfitting, and the lack of interpretability. To conclude, the review proposes methods that could improve the accuracy and availability of prediction by DL, such as learning from large and high-quality databases, establishing separate databases for groups with different features, optimizing and simplifying the algorithm structure, utilizing integrated multimodal data, and implementing visualization techniques.