Dental Caries Diagnosis Using Neural Networks and Deep Learning: A Systematic Review Publisher



Forouzeshfar P¹ ; Safaei AA^{2, 3} ; Ghaderi F^{3, 4} ; Hashemi Kamangar S⁵ ; Kaviani H⁶ ; Haghi S⁷ Show Affiliations
Source: Multimedia Tools and Applications Published:2024

Abstract

Dental caries is one of the oral health problems and the most common chronic infectious disease of childhood, and neural networks and artificial intelligence are increasingly being used in the field of dentistry. This review study aims to review studies published in the field of artificial intelligence and neural networks and dentistry. A search for studies in four databases, including Springer, ScienceDirect, PubMed (MedLine), and Institute of Electrical and Electronics Engineers (IEEE) was done. Finally, 28 studies were reviewed, most of which used Bitewing and Periapical images for the classification and detection of dental caries. The image databases ranged from 55 to 3000 and several evaluation metrics were used in the selected studies. The research questions were designed and reviewed based on PICOS (P stands for patient or problem, I stands for intervention, C stands for control or comparison, and O stands for outcomes). The majority of the studies also used pre-processing and data augmentation methods. The diversity between the networks used and the output evaluation criteria have made direct research comparisons challenging. The main focus of this research was on caries detection using deep learning methods and neural networks, especially convolutional neural networks that are suitable for images. The traditional methods of detecting caries, other than the methods based on artificial intelligence, have not been investigated in this research. Also, the main caries were interproximal and proximal caries in molars and premolars. The main difference between this and previous works is the use of more up-to-date articles (2016 to 2023) studies with an organized manner of reviewing, which is based on the types of images used. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.