Review of Dental Diagnosis by Deep Learning Models: Trends, Applications and Challenges

Abstract

Recently, deep learning (DL) has become increasingly important in the field of image processing, and its application in dental radiology has become particularly relevant. DL-based image recognition has become a key component of image interpretation in dental radiology, making it possible to automate diagnosis and treatment planning. These techniques can be applied to areas such as caries, tooth detection, periodontal disease, oral lesions, age determination, gender estimation, and osteoporosis. In addition, recent studies have shown that DL algorithms in these areas have high accuracy, with some studies achieving accuracy rates of over 90%. Such high accuracy can significantly improve the efficiency and accuracy of dental diagnostics and treatment planning. Thus, using DL in dental imaging may become an everyday tool in dentistry with the current advances in applications. In this paper, recent applications and potential challenges of using deep convolutional neural network architectures in dental image analysis will be reviewed.