Deep Learning Technologies in Dental Practice: Current Applications and Research Trends

Year 2025, EARLY VIEW, 1 - 1

Murat Can Şener , Hacer Karacan

https://doi.org/10.2339/politeknik.1726576

Abstract

The use of deep learning technologies in dental practice has been steadily increasing in recent years, accompanied by significant progress in related research. This study provides a comprehensive review of deep learning-based image processing techniques within the field of dentistry, with a particular focus on the performance of models applied in dental segmentation and classification tasks. The analysis reveals that architectures such as U-Net, Mask R-CNN, and YOLO have demonstrated high accuracy in detecting teeth, diseases, and lesions in dental radiographs. By systematically examining studies conducted between 2020 and 2025, this review highlights the potential of deep learning methods to enhance clinical diagnosis and treatment processes, emphasizing the growing importance of automated dental image analysis. The discussion section offers a detailed evaluation of the frequent use and success of U-Net, Mask R-CNN, and YOLO architectures, concluding that deep learning-based approaches can be effectively integrated into clinical workflows. These technologies play a critical role in the early diagnosis of dental pathologies and the development of personalized treatment plans.

Keywords

Dental segmentation, Dental classification, Deep learning, Panoramic radiography, Dental artificial intelligence applications

Ethical Statement

The author(s) of this article declare that the materials and methods used in this study do not require ethical committee permission and/or legal-special permission.

Diş Hekimliğinde Derin Öğrenme Teknolojileri: Güncel Uygulamalar ve Araştırma Trendleri

Abstract

Son yıllarda diş hekimliği uygulamalarında derin öğrenme teknolojilerinin kullanımı giderek artmakta ve bu alandaki araştırmalarda önemli ilerlemeler kaydedilmektedir. Bu çalışma, diş hekimliği alanında derin öğrenmeye dayalı görüntü işleme tekniklerini kapsamlı bir şekilde inceleyerek özellikle diş segmentasyonu ve sınıflandırma görevlerinde uygulanan modellerin performansına odaklanmaktadır. Yapılan analizler, U-Net, Mask R-CNN ve YOLO gibi mimarilerin diş radyografilerinde dişlerin, hastalıkların ve lezyonların tespitinde yüksek doğruluk sağladığını göstermektedir. 2020 ile 2025 yılları arasında gerçekleştirilen çalışmaları sistematik olarak inceleyen bu derleme, derin öğrenme yöntemlerinin klinik tanı ve tedavi süreçlerini geliştirme potansiyelini vurgulamakta ve otomatik diş görüntü analizinin artan önemini ortaya koymaktadır. Tartışma bölümünde, U-Net, Mask R-CNN ve YOLO mimarilerinin sık kullanımı ve başarıları detaylı şekilde değerlendirilmekte ve derin öğrenmeye dayalı yaklaşımların klinik iş akışlarına etkin bir şekilde entegre edilebileceği sonucuna varılmaktadır. Bu teknolojiler, diş patolojilerinin erken tanısında ve kişiselleştirilmiş tedavi planlarının geliştirilmesinde kritik bir rol oynamaktadır.

Keywords

Diş segmentasyonu, Diş sınıflandırması, Derin öğrenme, Panoramik radyografi, Diş hekimliğinde yapay zeka uygulamaları

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