Diagnostics 2023, 13, 2512. https://doi.org/10.3390/diagnostics13152512 www.mdpi.com/journal/diagnostics Systematic Review Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review Esra Sivari 1 , Guler Burcu Senirkentli 2 , Erkan Bostanci 3 , Mehmet Serdar Guzel 3 , Koray Acici 4 and Tunc Asuroglu 5, * 1 Department of Computer Engineering, Cankiri Karatekin University, Cankiri 18100, Turkey 2 Department of Pediatric Dentistry, Baskent University, Ankara 06810, Turkey 3 Department of Computer Engineering, Ankara University, Ankara 06830, Turkey 4 Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara 06830, Turkey 5 Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland * Correspondence: tunc.asuroglu@tuni.fi Abstract: Deep learning and diagnostic applications in oral and dental health have received significant a5ention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw a5ention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019–May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U- Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for be5er diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics. Keywords: deep learning; dental anomalies and diseases; dental diagnostics; dental images; convolutional neural network 1. Introduction Today, although most oral and dental diseases have early diagnosis and treatment opportunities with technological developments in oral and dental health, their global increase cannot be prevented. According to the WHO Global Oral Health Status Report (2022) [1], oral and dental diseases affect approximately 3.5 billion people worldwide. Especially in low- and middle-income countries, there are not adequate services in the field of oral and dental health due to the costs of diagnosis and treatment. As a result of this situation, it is estimated by the WHO that three out of four people in low- and middle- income countries are affected by oral and dental diseases [1]. The most common dental Citation: Sivari, E.; Senirkentli, G.B.; Bostanci, E.; Guzel, M.S.; Acici, K.; Asuroglu, T. Deep Learning in Diagnostic of Dental Anomalies and Diseases: A Systematic Review. Diagnostics 2023, 13, 2512. https://doi.org/10.3390/ diagnostics13152512 Academic Editor: Hakan Turkkahraman Received: 11 July 2023 Revised: 21 July 2023 Accepted: 25 July 2023 Published: 27 July 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, SwiMerland. This article is an open access article distributed under the terms and conditions of the Creative Commons A5ribution (CC BY) license (h5ps://creativecommons.org/license s/by/4.0/).