Oral Dental Diagnosis Using Deep Learning Techniques: A Review

The purpose of this study is to investigate the gradual incorporation of deep learning in the dental healthcare system, offering an easy and efficient diagnosis. For that, an electronic search was conducted in the Institute of Electrical and Electronics Engineers (IEEE) Xplore, ScienceDirect, Journal of Dentistry, Health Informatics Journal, and other credible resources. The studies varied with their tools and techniques used for the diagnosis while coping with the rapid deep-learning evolving base, with different types of conducting tools and analysis for the data. An inclusion criterion was set to specify the quality of the chosen papers. The papers included provided information about the neural network models used like model type, the targeted disease, and results evaluating parameters. The referenced databases ranged from 88 to 12600 clinical images. All the included studies used different neural network models with different outcome metrics. This inconsistency of the methods used makes them incomparable which makes reaching a reliable conclusion more complicated. The paper voiced some observations about the methods used with future recommendations. The goal is to review the deep learning methods that can be used in medical diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.