Ortodontide yapay zeka ve makine öğrenimi

Year 2019, Volume: 11 Issue: 4, 517 - 523, 01.12.2019

Süleyman Kutalmış Büyük , Sedanur Hatal

https://doi.org/10.21601/ortadogutipdergisi.547782

Abstract

Amaç: Bilgi teknolojisinin ortodontide klinik kullanımı son yıllarda önemli ölçüde artmıştır. Bu sistematik derlemenin amacı, ortodonti alanındaki yapay zeka ve makine öğreniminin bilimsel bir analizini yapmaktır.
Gereç ve Yöntem: 25 Eylül 2018 tarihinde ortodonti yapay zeka ve makine öğrenimi hakkında elektronik arama ve el ile arama işlemleri gerçekleştirilmiştir.
Bulgular: Toplam 107 çalışma bulunmuştur. Dokuz çalışma, duplikasyon nedeniyle hariç tutulmuştur. Yazım dili İngilizce olmayan ve konuyla ilgisi olmayan makaleler hariç tutulduktan sonra, bu sistematik derleme için 23 tam metin makale incelenmiştir. Bu sistematik derlemeye 3 makale daha eklenmiştir. On iki otomatik sefalometrik işaret belirleme, 6 ortodontik tanı ve tedavi sonuçları, 2 ortodontik diş çekimi kararı, 3 yüz çekiciliği, 1 headgear seçimi, 1 dokunmatik sterilizasyon sistemi ve 1 otomatik iskelet yaşı tayini bu sistematik derlemede yer almıştır.
Sonuçlar: Yapay zeka ve makine öğrenimi, esas olarak otomatik sefalometrik nokta belirleme, yüz çekiciliği ve ortodontik amaç için diş çekimi kararlarının belirlenmesine odaklanmaktadır. Yapay zekanın ortodontide kullanılmasının klinik olarak daha doğru ve hızlı sonuçlar elde edilmesi açısından önem taşımaktadır.

Keywords

yapay zeka, makine öğrenmesi, ortodonti

Artificial intelligence and machine learning in orthodontics

Abstract

Objective: The clinical use of information technology in orthodontics has increased significantly in recent years. The aim of this systematic review is to perform a scientific analysis of artificial intelligence and machine learning in orthodontics.
Methods: An electronic search and manual search were performed on September 25, 2018 about using artificial intelligence and machine learning in orthodontics.
Results: A total of 107 studies were found. Nine studies were excluded because of duplication. After exclusion of all the irrelevant and non-English articles, 23 full-text articles remained to be included in this systematic review. 3 additional articles were included in this systematic review. Twelve automatic cephalometric landmark determination, 6 orthodontic diagnosis and treatment outcomes, 2 orthodontic tooth extraction decision, 3 facial attractiveness, 1 headgear selection, 1 touchless sterilisation system and 1 automatic skeletal age determination studies were included in this systematic review.
Conclusions: Artificial intelligence and machine learning are mainly focused on determination of automatic cephalometric points, facial attractiveness and tooth extraction decisions for orthodontic purposes. The use of artificial intelligence in orthodontics is important in terms of obtaining more accurate and rapid results clinically.

Keywords

artificial intelligence, machine learning, orthodontics

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