Minireviews
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Med Imaging. Dec 28, 2021; 2(6): 104-114
Published online Dec 28, 2021. doi: 10.35711/aimi.v2.i6.104
Application of machine learning in oral and maxillofacial surgery
Kai-Xin Yan, Lei Liu, Hui Li
Kai-Xin Yan, Lei Liu, Hui Li, State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Yan KX, Liu L, and Li H contributed to drafting the paper; Yan KX and Liu L contributed to the literature review; Yan KX wrote this paper as the first author; Li H contributed to critical revision and editing of the manuscript, and gave approval to the final version as the corresponding author.
Supported by National Natural Science Foundation of China, No. 82100961.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior author or other coauthors who contributed their efforts in this manuscript.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hui Li, MD, PhD, Assistant Professor, State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, No. 14 Section 3 Renminnan Road, Chengdu 610041, Sichuan Province, China. 475393040@qq.com
Received: December 7, 2021
Peer-review started: December 7, 2021
First decision: December 13, 2021
Revised: December 20, 2021
Accepted: December 28, 2021
Article in press: December 28, 2021
Published online: December 28, 2021
Processing time: 21 Days and 4.5 Hours
Abstract

Oral and maxillofacial anatomy is extremely complex, and medical imaging is critical in the diagnosis and treatment of soft and bone tissue lesions. Hence, there exists accumulating imaging data without being properly utilized over the last decades. As a result, problems are emerging regarding how to integrate and interpret a large amount of medical data and alleviate clinicians’ workload. Recently, artificial intelligence has been developing rapidly to analyze complex medical data, and machine learning is one of the specific methods of achieving this goal, which is based on a set of algorithms and previous results. Machine learning has been considered useful in assisting early diagnosis, treatment planning, and prognostic estimation through extracting key features and building mathematical models by computers. Over the past decade, machine learning techniques have been applied to the field of oral and maxillofacial surgery and increasingly achieved expert-level performance. Thus, we hold a positive attitude towards developing machine learning for reducing the number of medical errors, improving the quality of patient care, and optimizing clinical decision-making in oral and maxillofacial surgery. In this review, we explore the clinical application of machine learning in maxillofacial cysts and tumors, maxillofacial defect reconstruction, orthognathic surgery, and dental implant and discuss its current problems and solutions.

Keywords: Radiography; Artificial intelligence; Machine learning; Deep learning; Oral surgery; Maxillofacial surgery

Core Tip: A dramatic increase in medical imaging data has exceeded the ability of clinicians to process and analyze, which calls for higher-level analytic tools. Machine learning-based image analysis is useful for extracting key information to improve diagnostic accuracy and treatment efficacy. In this review, we summarize the applications of machine learning in oral and maxillofacial surgery as well as its current problems and solutions.