Detecting dental caries on oral photographs using artificial intelligence: A systematic review

Abstract

ObjectivesThis systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs.MethodsMethodological characteristics and performance metrics of clinical studies reporting on deep learning and other machine learning algorithms were assessed. The risk of bias was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS‐2) tool. A systematic search was conducted in EMBASE, Medline, and Scopus.ResultsOut of 3410 identified records, 19 studies were included with six and seven studies having low risk of biases and applicability concerns for all the domains, respectively. Metrics varied widely and were assessed on multiple levels. F1‐scores for classification and detection tasks were 68.3%–94.3% and 42.8%–95.4%, respectively. Irrespective of the task, F1‐scores were 68.3%–95.4% for professional cameras, 78.8%–87.6%, for intraoral cameras, and 42.8%–80% for smartphone cameras. Limited studies allowed assessing AI performance for lesions of different severity.ConclusionAutomatic detection of dental caries using AI may provide objective verification of clinicians' diagnoses and facilitate patient‐clinician communication and teledentistry. Future studies should consider more robust study designs, employ comparable and standardized metrics, and focus on the severity of caries lesions.

Informatic tools for diagnosis in dentistry. A compilation review

“…Computer tools for diagnosing tooth decay from images offer objective verification, aid in doctor-patient communication, teledentistry, and potentially improve diagnostic accuracy and efficiency in the detection of oral diseases. (61) Studies such as Tareq et al (62) argue that these applications make it possible to predict dental cavitations from non-standardized photographs with reasonable clinical accuracy, improving access to oralhealthcaree in resource-limited areas. In addition, the use of deep learning in panoramic images makes it possible to accurately detect various tooth-related diseases in real-time, helping to plan treatment in time and reducing the risk of misdiagnosis.…”

ChatGPT‐4 as auxiliary tool in the temporomandibular disorders diagnostic: An opinion

“…4,7 In dentistry, various AI models have been employed for different purposes, including the detection of dental caries through oral photographs, the identification of fractures in reimplanted teeth using panoramic and periapical radiographic images and the prognostic prediction of head and neck cancer using digitized histological images. [8][9][10] In March 2023, OpenAI released ChatGPT-4, a software structured on large language models (LLMs), with the potential to generate human-like text on an endless range of topics, as well as their markable ability to describe objects contained in images with a high degree of accuracy. 6 Operationally, this tool works by extracting information from the Web and other servers or by accessing the provider's own databases.…”

Comment on: Detecting dental caries on oral photographs using artificial intelligence: A systematic review

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We highly appreciate the work of Moharrami et al (2023) which addresses an important issue of far-reaching significance. We are all the more inspired-based on the observations and conclusions made by the authors-to add some further considerations.

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