SynthesisJournal of healthcare engineering2022
Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries.
Synthesis in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 4 syntheses or guidelines pooled it, 52 citations in OpenAlex.
- Radiographic choice for skeletal maturation assessment: a systematic review of Cervical Vertebrae Method and Middle Phalanx Method.Oral radiology · 2026Pooled it
- Pooled it
- Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.Clinical and experimental dental research · 2024Pooled it
- AI Quality Standards in Health Care: Rapid Umbrella Review.Journal of medical Internet research · 2024Pooled it
- Charting the artificial intelligence revolution in orthodontics : A bibliometric analysis of key players, collaborations, and research directions.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2026Review
- Photonic Methods for the Assessment of Lesion Activity.Diagnostics (Basel, Switzerland) · 2026Review
- A Dual-Branch Deep Learning Framework with Explainability for Dental Caries Classification Using Intra-Oral Photographs and Radiographs.Journal of imaging · 2026Article
- Accuracy of deep learning-based AI models for early caries lesion detection: the influence of annotation quality and reference choice.Clinical oral investigations · 2025Article
- Evaluation of Artificial Intelligence-Based Diagnostic Tools for Accurate Detection and Classification of Oral Lesions: A Clinical Study.Journal of pharmacy & bioallied sciences · 2025Article
- Article
- Comparing diagnostic skills in endodontic cases: dental students versus ChatGPT-4o.BMC oral health · 2025Article
- Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8.Scientific reports · 2025Article
- Patients' attitudes toward artificial intelligence in dentistry and their trust in dentists.Oral radiology · 2025Article
- Early childhood caries (ECC) prediction models using Machine Learning.Journal of clinical and experimental dentistry · 2024Article
- Knowledge, attitude, and practice of dental patients toward dental defects and dental fillings in Jinan, Shandong Province, China: a mediation analysis.BMC public health · 2024Article
- Application of Artificial Intelligence in Pediatric Dentistry: A Literature Review.Journal of pharmacy & bioallied sciences · 2024Review
- Analysis of Pediatric Pulpotomy, Pulpectomy, and Extractions in Primary Teeth Revealed No Significant Association with Subsequent Root Canal Therapy and Extractions in Permanent Teeth: A Retrospective Study.Pediatric reports · 2024Article
- Review
- Artificial intelligence enabled smart phone app for real-time caries detection on bitewing radiographs.Bioinformation · 2024Article
- Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review.PeerJ. Computer science · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make dental care better, smoother, and time-saving for professionals. AI helps the dentistry professionals to fulfil demands of patients and to ensure quality treatment and better oral health care. AI can also help in predicting failures of clinical cases and gives reliable solutions. In this way, it helps in reducing morbidity ratio and increasing quality treatment of dental problem in population. Objectives: The main objective of this study is to conduct a systematic review of studies concerning the association between dental caries and machine learning. The objective of this study is to design according to the PICO criteria. Materials and Methods: A systematic search for randomized trials was conducted under the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this study, e-search was conducted from four databases including PubMed, IEEE Xplore, Science Direct, and Google Scholar, and it involved studies from year 2008 to 2022. Result: This study fetched a total of 133 articles, from which twelve are selected for this systematic review. We analyzed different types of machine learning algorithms from which deep learning is widely used with dental caries images dataset. Neural Network Backpropagation algorithm, one of the deep learning algorithms, gives a maximum accuracy of 99%. Conclusion: In this systematic review, we concluded how deep learning has been applied to the images of teeth to diagnose the detection of dental caries with its three types (proximal, occlusal, and root caries). Considering our findings, further well-designed studies are needed to demonstrate the diagnosis of further types of dental caries that are based on progression (chronic, acute, and arrested), which tells us about the severity of caries, virginity of lesion, and extent of caries. Apart from dental caries, AI in the future will emerge as supreme technology to detect other diseases of oral region combinedly and comprehensively because AI will easily analyze big datasets that contain multiple records.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.