ABSTRACT
Introduction: The role of artificial intelligence (AI) is currently increasing in terms of diagnosing diseases and planning treatment in endodontics. However, findings from individual research studies are not systematically reviewed and compiled together. Hence, this study aimed to systematically review, appraise, and evaluate neural AI algorithms employed and their comparative efficacy to conventional methods in endodontic diagnosis and treatment planning. Methods: The present research question focused on the literature search about different AI algorithms and models of AI assisted endodontic diagnosis and treatment planning. The search engine included databases such as Google Scholar, PubMed, and Science Direct with search criteria of primary research paper, published in English, and analyzed data on AI and its role in the field of endodontics. Results: The initial search resulted in 785 articles, exclusion based on abstract relevance, animal studies, grey literature and letter to editors narrowed down the scope of selected articles to 11 accepted for review. The review data supported the findings that AI can play a crucial role in the area of endodontics, such as identification of apical lesions, classifying and numbering teeth, detecting dental caries, periodontitis and periapical disease, diagnosing different dental problems, helping dentists make referrals, and also helping them make plans for treatment of dental disorders in a timely and effective manner with greater accuracy. Conclusion: AI with different models or frameworks and algorithms can help dentists to diagnose and manage endodontic problems with greater accuracy. However, endodontic fraternity needs to provide more emphasis on the utilization of AI, provision of evidence based guidelines and implementation of the AI models.
ABSTRACT
PURPOSE: The prevalence of the middle distal (MD) canal in the mandibular molar is significantly low among countries including the USA, Spain, Turkey, and Jordan; however, analysis of its prevalence and configuration has not been performed in Saudi Arabia. Therefore, we aimed to assess the prevalence and configuration of the MD canal in Saudi Arabia. METHODS: A retrospective analysis of 132 cone-beam computed tomography (CBCT) images was performed to evaluate the presence of the MD canal in patients visiting the Radiology Department of The College of Dentistry in King Saud University between July 2018 and July 2019. The canal was examined if it was confluent with the distobuccal (DB) or distolingual (DL) canals, fin, or independent. Moreover, the distances from the MD canal to the DL and DB canals and cementoenamel junction (CEJ) were recorded. RESULTS: One MD canal (0.7%) was observed in 145 teeth. It was confluent with the DL canal. No statistical significance was observed among sex and age. The distances from the MD canal to the DL canal, DB canal, and CEJ were 1.4 mm, 1.9 mm, and 3.1 mm, respectively. CONCLUSIONS: The prevalence of the MD canal was significantly low in a Saudi subpopulation (0.7%). Careful evaluation of CBCT images and the pulpal floor is significantly important to detect the MD canal to ensure a good prognosis.
