Artificial intelligence for oral and maxillo-facial surgery: A narrative review

被引:20
|
作者
Rasteau, Simon [1 ]
Ernenwein, Didier [2 ]
Savoldelli, Charles [3 ]
Bouletreau, Pierre [1 ]
机构
[1] Claude Bernard Lyon 1 Univ, Lyon Sud Hosp, Hosp Civils Lyon, Maxillofacial Surg Facial Plast Surg Stomatol & S, 165 Chemin Grand Revoyet, F-69310 Pierre Benite, France
[2] Paris Diderot Univ, Childrens Hosp Robert Debre, Dept Pediat Oral & Maxillofacial & Plast Surg, Paris, France
[3] Cote dAzur Univ, Nice Univ Hosp, Univ Inst Face & Neck, 31 Ave Valombrose, F-06100 Nice, France
关键词
Artificial intelligence; Machine learning; Deep learning; Artificial neural network; Computer aided diagnosis; Oral and maxillofacial surgery; NEURAL-NETWORKS; CANCER; CLASSIFICATION; DIAGNOSIS;
D O I
10.1016/j.jormas.2022.01.010
中图分类号
R78 [口腔科学];
学科分类号
1003 ;
摘要
Artificial Intelligence (Al) is a set of technologies that simulate human cognition in order to address a specific problem. The improvement in computing speed, the exponential production and the routine collection of data have led to the rapid development of Al in the health sector. In this review, we propose to provide surgeons with the essential technical elements to help them understand the possibilities offered by Al and to review the current applications of Al for oral and maxillofacial surgery (OMFS). The review of the literature reveals a real research boom of Al in all fields in OMFS. The algorithms used are related to machine learning, with a strong representation of the convolutional neural networks specific to deep learning. The complex architecture of these networks gives them the capacity to extract and process the elementary characteristics of an image, and they are therefore particularly used for diagnostic purposes on medical imagery or facial photography. We identified representative articles dealing with Al algorithms providing assistance in diagnosis, therapeutic decision, preoperative planning, or prediction and evaluation of the outcomes. Thanks to their learning, classification, prediction and detection capabilities, Al algorithms complement human skills while limiting their imperfections. However, these algorithms should be subject to rigorous clinical evaluation, and ethical reflection on data protection should be systematically conducted. (C) 2022 Elsevier Masson SAS. All rights reserved.
引用
收藏
页码:276 / 282
页数:7
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