Deep learning for caries detection: A systematic review

Mohammad‐Rahimi, Hossein; Motamedian, Saeed Reza; Rohban, Mohammad Hossein; Krois, Joachim; Uribe, Sergio; Nia, Erfan Mahmoudi; Rokhshad, Rata; Nadimi, Mohadeseh; Schwendicke, Falk

doi:10.1016/j.jdent.2022.104115

Cited by 112 publications

(91 citation statements)

References 55 publications

(48 reference statements)

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“…There were 14 systematic reviews published in the last two years focused on AI in dentistry [ 119 , 198 , 199 , 200 , 201 ]. Only three of them were focused on dentistry with a general scope.…”

Section: Discussionmentioning

confidence: 99%

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

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This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

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Section: Discussionmentioning

confidence: 99%

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

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“…Applications of AI in medicine include the detection and classification of pathologies on imagery (radiographs and photographs), the prediction of events, or the simulation of drug‐target interactions, among others 1 . Research on the use of AI in dentistry has grown in parallel to medicine, and the potential of AI for various diagnostic purposes such as detection of dental caries on radiographs, assessment of the difficulty of endodontic cases, automated localization of cephalometric landmarks, and segmentation of maxillofacial cysts and tumors has been shown 2‐4 . Most AI applications are built on machine learning, where mathematical models are trained to detect statistical association patterns in a dataset to make predictions.…”

Section: Introductionmentioning

confidence: 99%

“…assessment of the difficulty of endodontic cases, automated localization of cephalometric landmarks, and segmentation of maxillofacial cysts and tumors has been shown. [2][3][4] Most AI applications are built on machine learning, where mathematical models are trained to detect statistical association patterns in a dataset to make predictions. Deep learning is a subgroup of machine learning implemented via multi-layered neural networks.…”

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confidence: 99%

Deep learning in periodontology and oral implantology: A scoping review

Mohammad‐Rahimi

Motamedian

Pirayesh

et al. 2022

J of Periodontal Research

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Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta‐analysis was performed. The risk of bias was assessed using the QUADAS‐2 tool. We included 47 studies: focusing on imaging data (n = 20) and non‐imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi‐layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.

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“…Machine learning (ML) and artificial intelligence have been reported as a potential solution for highly accurate and rapid detection and scoring of dental caries [Mohammad-Rahimi et al, 2022]. Most studies have been using radiographic images with accuracies exceeding 90%, yet these studies lack scoring of lesion severity or activity and are dependent on radiographs being obtained and the resolution limits of radiography [Wenzel and Fejerskov, 1993;Laishram et al, 2020;Mohammad-Rahimi et al, 2022]. One study has been reported using an intraoral camera to obtain white light images to detect and score occlusal lesions using ICDAS [Moutselos et al, 2019].…”

Section: Introductionmentioning

confidence: 99%

Convolution Neural Networks and Targeted Fluorescent Nanoparticles to Detect and ICDAS Score Caries

Jones

Jones²,

Tenuta

et al. 2022

Caries Res

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Previous work has shown targeted fluorescent starch nanoparticles (TFSNs) can label the subsurface of carious lesions and assist dental professionals in the diagnostic process. In this study, we aimed to evaluate the potential of using artificial intelligence (AI) to detect and score carious lesions using the ICDAS in combination with fluorescent imaging following application of TFSNs on teeth with a range of lesion severities, using ICDAS-labeled images as the reference standard. A total of 130 extracted human teeth with ICDAS scores from 0 to 6 were selected by a calibrated cariologist according to ICDAS. Then, the same surface was imaged with a stereomicroscope under white-light illumination, and blue-light illumination with an orange filter following application of the TFSNs. Both sets of images were labeled by another blinded ICDAS-calibrated cariologist to demarcate lesion position and severity. Convolutional Neural Networks, state-of-the-art models in imaging AI, were trained to determine the presence, location, ICDAS score (severity), and lesion surface porosity (as an indicator of activity) of carious lesions, and tested by 30 k-fold validation for white-light, blue-light, and the combined image sets. The best models showed high performance for the detection of carious lesions (sensitivity 80.26%, PPV 76.36%) and potential for determining the severity via ICDAS scoring (accuracy 72%, SD 5.67%) and the detection of surface porosity as an indicator of the activity of the lesions (accuracy 90%, SD 7.00%). More broadly, the combination of targeted biopolymer nanoparticles with imaging AI is a promising combination of novel technologies that could be applied to many other applications.

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Deep learning for caries detection: A systematic review

Cited by 112 publications

References 55 publications

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

Deep learning in periodontology and oral implantology: A scoping review

Convolution Neural Networks and Targeted Fluorescent Nanoparticles to Detect and ICDAS Score Caries

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