Applications of Artificial Intelligence in Orthodontics-An Overview and Perspective Based on the Current State of the Art

被引:13
作者
Kunz, Felix [1 ]
Stellzig-Eisenhauer, Angelika [1 ]
Boldt, Julian [2 ]
机构
[1] Univ Hosp Wurzburg, Dept Orthodont, D-97070 Wurzburg, Germany
[2] Univ Hosp Wurzburg, Dept Prosthet Dent, D-97070 Wurzburg, Germany
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 06期
关键词
orthodontics; artificial intelligence; machine learning; deep learning; cephalometry; age determination by skeleton; tooth extraction; orthognathic surgery; CERVICAL VERTEBRAL MATURATION; SKELETAL MATURATION; DECISION-MAKING; NEURAL NETWORKS; CVM METHOD; EXTRACTION; CLASSIFICATION; DIAGNOSIS; AGE; RELIABILITY;
D O I
10.3390/app13063850
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Featured Application The aim of this article is to provide the reader with an overview of current applications of AI in orthodontics. Artificial intelligence (AI) has already arrived in many areas of our lives and, because of the increasing availability of computing power, can now be used for complex tasks in medicine and dentistry. This is reflected by an exponential increase in scientific publications aiming to integrate AI into everyday clinical routines. Applications of AI in orthodontics are already manifold and range from the identification of anatomical/pathological structures or reference points in imaging to the support of complex decision-making in orthodontic treatment planning. The aim of this article is to give the reader an overview of the current state of the art regarding applications of AI in orthodontics and to provide a perspective for the use of such AI solutions in clinical routine. For this purpose, we present various use cases for AI in orthodontics, for which research is already available. Considering the current scientific progress, it is not unreasonable to assume that AI will become an integral part of orthodontic diagnostics and treatment planning in the near future. Although AI will equally likely not be able to replace the knowledge and experience of human experts in the not-too-distant future, it probably will be able to support practitioners, thus serving as a quality-assuring component in orthodontic patient care.
引用
收藏
页数:10
相关论文
共 96 条
[71]   CephaNN: A Multi-Head Attention Network for Cephalometric Landmark Detection [J].
Qian, Jiahong ;
Luo, Weizhi ;
Cheng, Ming ;
Tao, Yubo ;
Lin, Jun ;
Lin, Hai .
IEEE ACCESS, 2020, 8 :112633-112641
[72]   Consistency of orthodontic extraction decisions [J].
Ribarevski, R ;
Vig, P ;
Vig, KD ;
Weyant, R ;
OBrien, K .
EUROPEAN JOURNAL OF ORTHODONTICS, 1996, 18 (01) :77-80
[73]   Comparison of AudaxCeph?'s fully automated cephalometric tracing technology to a semi-automated approach by human examiners [J].
Ristau, Britta ;
Coreil, Mark ;
Chapple, Andrew ;
Armbruster, Paul ;
Ballard, Richard .
INTERNATIONAL ORTHODONTICS, 2022, 20 (04)
[74]  
Sabri Roy, 2006, World J Orthod, V7, P177
[75]   Accuracy of digital and analogue cephalometric measurements assessed with the sandwich technique [J].
Santoro, M ;
Jarjoura, K ;
Cangialosi, TJ .
AMERICAN JOURNAL OF ORTHODONTICS AND DENTOFACIAL ORTHOPEDICS, 2006, 129 (03) :345-351
[76]   Deep learning for cephalometric landmark detection: systematic review and meta-analysis [J].
Schwendicke, Falk ;
Chaurasia, Akhilanand ;
Arsiwala, Lubaina ;
Lee, Jae-Hong ;
Elhennawy, Karim ;
Jost-Brinkmann, Paul-Georg ;
Demarco, Flavio ;
Krois, Joachim .
CLINICAL ORAL INVESTIGATIONS, 2021, 25 (07) :4299-4309
[77]   Convolutional neural networks for dental image diagnostics: A scoping review [J].
Schwendicke, Falk ;
Golla, Tatiana ;
Dreher, Martin ;
Krois, Joachim .
JOURNAL OF DENTISTRY, 2019, 91
[78]   Comparison of Deep Learning Models for Cervical Vertebral Maturation Stage Classification on Lateral Cephalometric Radiographs [J].
Seo, Hyejun ;
Hwang, JaeJoon ;
Jeong, Taesung ;
Shin, Jonghyun .
JOURNAL OF CLINICAL MEDICINE, 2021, 10 (16)
[79]   Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals [J].
Shin, WooSang ;
Yeom, Han-Gyeol ;
Lee, Ga Hyung ;
Yun, Jong Pil ;
Jeong, Seung Hyun ;
Lee, Jong Hyun ;
Kim, Hwi Kang ;
Kim, Bong Chul .
BMC ORAL HEALTH, 2021, 21 (01)
[80]   Automatic Cephalometric Landmark Detection on X-ray Images Using a Deep-Learning Method [J].
Song, Yu ;
Qiao, Xu ;
Iwamoto, Yutaro ;
Chen, Yen-wei .
APPLIED SCIENCES-BASEL, 2020, 10 (07)