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Dental disease evaluation and clinical assessment are frequently accomplished through radiographic penetration. The difficulty of obtaining an accurate clinical diagnosis from radiographs rises due to the minimal mineral density change in demineralized tissue of tooth and gum disorders. Dental abnormalities may not be visible on radiographs until the demineralization is higher than 40%, according to the literature. As a result, a dental practitioner’s judgment can have a big impact on how accurately the radiography penetration depth is determined through visual inspection. To counteract this effect, image processing-based clinical diagnosis methods have become widely adopted, transforming dentistry from traditional to advance in recent years. The efforts made in the area of image processing-based digital dental diagnosis of the most challenging dental issues are outlined in the presented comprehensive literature evaluation, which also identifies any research gaps in the scope of work already done. The included studies’ quality was evaluated using Quality Assessment and Diagnostic Accuracy Tool-2 (QUADAS-2). A total of 52 out of 178 articles, published from 2012 to February 2023, were reviewed and data like image-processing approach, the size of datasets, approach results, advantages and disadvantages, name(s) of diagnosed diseases, imaging type, author, and publication year were extracted. Results show that, in 52 studies, more than 14 image-processing approaches were used on different types of radiographs for the diagnosis of a single or more than one disease by a single approach with an accuracy range from 64% to 93%. Most studies have used artificial intelligence (AI) for computer-aided diagnosis and used dental experts to label their dataset and validate the outcome of proposed methods. Efforts done by different research groups for image processing-based digital diagnosis are appreciable but still, they are lagging to meet clinically expected accuracy. There looks to be a great requirement for the development or standardization of existing methodology and it is also needed to construct standard public dental datasets to attract a greater number of research groups in the dental field.
Dental disease evaluation and clinical assessment are frequently accomplished through radiographic penetration. The difficulty of obtaining an accurate clinical diagnosis from radiographs rises due to the minimal mineral density change in demineralized tissue of tooth and gum disorders. Dental abnormalities may not be visible on radiographs until the demineralization is higher than 40%, according to the literature. As a result, a dental practitioner’s judgment can have a big impact on how accurately the radiography penetration depth is determined through visual inspection. To counteract this effect, image processing-based clinical diagnosis methods have become widely adopted, transforming dentistry from traditional to advance in recent years. The efforts made in the area of image processing-based digital dental diagnosis of the most challenging dental issues are outlined in the presented comprehensive literature evaluation, which also identifies any research gaps in the scope of work already done. The included studies’ quality was evaluated using Quality Assessment and Diagnostic Accuracy Tool-2 (QUADAS-2). A total of 52 out of 178 articles, published from 2012 to February 2023, were reviewed and data like image-processing approach, the size of datasets, approach results, advantages and disadvantages, name(s) of diagnosed diseases, imaging type, author, and publication year were extracted. Results show that, in 52 studies, more than 14 image-processing approaches were used on different types of radiographs for the diagnosis of a single or more than one disease by a single approach with an accuracy range from 64% to 93%. Most studies have used artificial intelligence (AI) for computer-aided diagnosis and used dental experts to label their dataset and validate the outcome of proposed methods. Efforts done by different research groups for image processing-based digital diagnosis are appreciable but still, they are lagging to meet clinically expected accuracy. There looks to be a great requirement for the development or standardization of existing methodology and it is also needed to construct standard public dental datasets to attract a greater number of research groups in the dental field.
BACKGROUND Oral diseases have been described by the World Health Organization (WHO) as the most prevalent diseases globally, affecting some 3.5 billion people. This leads to significant health and economic burdens and can impact the quality of life of affected individuals. Therefore, dentists have a great responsibility to efficiently diagnose and determine the best treatment option. However, some do not have the experience and knowledge to make the right clinical decisions. For this reason, artificial intelligence (AI) techniques, mainly rule-based systems, have been used in dentistry to aid physicians in making faster and more reliable decisions. OBJECTIVE This scoping review aims to explore and summarize the application of rule-based systems widely employed in dentistry and to evaluate their performance and practical significance. METHODS We conducted a scoping review following the methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) on five databases: Web of Science, Scopus, Google Scholar, Saudi Digital Library, and the IEEE Xplore. We searched for literature published in English up to October 2021. Two reviewers evaluated each potentially relevant study for inclusion/exclusion criteria, and any discrepancies were resolved by a third researcher. RESULTS Of 303 studies, 19 fulfilled this review’s inclusion criteria. We identified two domains based on the methodology used in the included studies: (i) uncertainty management approaches employed in the rule-based system (n = 16) and (ii) integrating machine learning techniques with the rule-based system (n = 5). The vast majority of included publications used fuzzy logic to manage uncertainty (n = 11). A hybrid fuzzy rule-based system and neural network achieved the highest accuracy of 96%. From a medical perspective, the articles were aimed at diagnosis (n = 11), treatment (n = 3), and both diagnosis and treatment (n = 4), while less attention was paid to detection and classification (n = 1). The review also found that periodontology was the most commonly addressed specialty. CONCLUSIONS In an analysis of the current literature, rule-based systems were found reliable to assist dental practitioners in decision-making. Clinical decision-making involves a high level of uncertainty, which explains the tendency to use fuzzy logic in rule-based systems. These systems can also be used as educational tools primarily for both dental interns and less experienced general dentists to aid in making reliable decisions.
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