SynthesisThe Cochrane database of systematic reviews2021
Imaging modalities to inform the detection and diagnosis of early caries.
Synthesis in The Cochrane database of systematic reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Comparison Between Detection of Orthodontic-Related Carious Lesions With Fluorescence or Visual Method: A Systematic Review.International journal of dental hygiene · 2026Pooled it
- American Dental Association and American Academy of Oral and Maxillofacial Radiology patient selection for dental radiography and cone-beam computed tomography: Clinical recommendations.Oral surgery, oral medicine, oral pathology and oral radiology · 2026Guideline
- American Dental Association and American Academy of Oral and Maxillofacial Radiology patient selection for dental radiography and cone-beam computed tomography: Clinical recommendations.Journal of the American Dental Association (1939) · 2026Guideline
- Visual or visual-tactile examination to detect and inform the diagnosis of enamel caries.The Cochrane database of systematic reviews · 2021Pooled it
- From Clinic to Community: An Interpretable Artificial Intelligence Framework for Enamel Caries Detection to Support Public Health Dentistry.European journal of dentistry · 2026Article
- Comparative assessment of occlusal and proximal caries detection using an intraoral scanner and panoramic radiography.BMC oral health · 2026Article
- Teledentistry in Pediatric Dental Care During Periods of Restricted Access: A Pilot Feasibility Study.Journal of clinical medicine · 2026Article
- Automated Tooth Detection and Caries Identification in CBCT With Deep Learning.International dental journal · 2026Article
- Assessing the Efficacy of Artificial Intelligence Platforms in Answering Dental Caries Multiple-Choice Questions: A Comparative Study of ChatGPT and Google Gemini Language Models.Dentistry journal · 2026Article
- Article
- Practice-Level Variation in the Provision of Subsidised Dental Services to Adult Danes in 2019: A Register-Based Study.Community dentistry and oral epidemiology · 2025Article
- The role of artificial intelligence in diagnosing pediatric dental disorders-a narrative review.Frontiers in dental medicine · 2025Review
- Article
- Cariostatic Agents: From Silver Diamine Fluoride to Emerging Bioactive Compounds.Clinical, cosmetic and investigational dentistry · 2025Review
- Using AI in Optimizing Oral and Dental Diagnoses-A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- Classification of Caries Based on CBCT: A Deep Learning Network Interpretability Study.Journal of imaging informatics in medicine · 2024Article
- Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis.The Japanese dental science review · 2024Review
- The Use of Artificial Intelligence in Caries Detection: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- ORCA-EFCD consensus report on clinical recommendation for caries diagnosis. Paper I: caries lesion detection and depth assessment.Clinical oral investigations · 2024Article
- Comparison of artificial intelligenceQuantitative imaging in medicine and surgery · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe detection and diagnosis of caries at the earliest opportunity is fundamental to the preservation of tooth tissue and maintenance of oral health. Radiographs have traditionally been used to supplement the conventional visual-tactile clinical examination. Accurate, timely detection and diagnosis of early signs of disease could afford patients the opportunity of less invasive treatment with less destruction of tooth tissue, reduce the need for treatment with aerosol-generating procedures, and potentially result in a reduced cost of care to the patient and to healthcare services.
objectivesTo determine the diagnostic accuracy of different dental imaging methods to inform the detection and diagnosis of non-cavitated enamel only coronal dental caries. SEARCH
methodsCochrane Oral Health's Information Specialist undertook a search of the following databases: MEDLINE Ovid (1946 to 31 December 2018); Embase Ovid (1980 to 31 December 2018); US National Institutes of Health Ongoing Trials Register (ClinicalTrials.gov, to 31 December 2018); and the World Health Organization International Clinical Trials Registry Platform (to 31 December 2018). We studied reference lists as well as published systematic review articles. SELECTION CRITERIA: We included diagnostic accuracy study designs that compared a dental imaging method with a reference standard (histology, excavation, enhanced visual examination), studies that evaluated the diagnostic accuracy of single index tests, and studies that directly compared two or more index tests. Studies reporting at both the patient or tooth surface level were included. In vitro and in vivo studies were eligible for inclusion. Studies that explicitly recruited participants with more advanced lesions that were obviously into dentine or frankly cavitated were excluded. We also excluded studies that artificially created carious lesions and those that used an index test during the excavation of dental caries to ascertain the optimum depth of excavation. DATA COLLECTION AND ANALYSIS: Two review authors extracted data independently and in duplicate using a standardised data extraction form and quality assessment based on QUADAS-2 specific to the clinical context. Estimates of diagnostic accuracy were determined using the bivariate hierarchical method to produce summary points of sensitivity and specificity with 95% confidence regions. Comparative accuracy of different radiograph methods was conducted based on indirect and direct comparisons between methods. Potential sources of heterogeneity were pre-specified and explored visually and more formally through meta-regression. MAIN
resultsWe included 104 datasets from 77 studies reporting a total of 15,518 tooth sites or surfaces. The most frequently reported imaging methods were analogue radiographs (55 datasets from 51 studies) and digital radiographs (42 datasets from 40 studies) followed by cone beam computed tomography (CBCT) (7 datasets from 7 studies). Only 17 studies were of an in vivo study design, carried out in a clinical setting. No studies were considered to be at low risk of bias across all four domains but 16 studies were judged to have low concern for applicability across all domains. The patient selection domain had the largest number of studies judged to be at high risk of bias (43 studies); the index test, reference standard, and flow and timing domains were judged to be at high risk of bias in 30, 12, and 7 studies respectively. Studies were synthesised using a hierarchical bivariate method for meta-analysis. There was substantial variability in the results of the individual studies, with sensitivities that ranged from 0 to 0.96 and specificities from 0 to 1.00. For all imaging methods the estimated summary sensitivity and specificity point was 0.47 (95% confidence interval (CI) 0.40 to 0.53) and 0.88 (95% CI 0.84 to 0.92), respectively. In a cohort of 1000 tooth surfaces with a prevalence of enamel caries of 63%, this would result in 337 tooth surfaces being classified as disease free when enamel caries was truly present (false negatives), and 43 tooth surfaces being classified as diseased in the absence of enamel caries (false positives). Meta-regression indicated that measures of accuracy differed according to the imaging method (Chi AUTHORS'
conclusionsThe design and conduct of studies to determine the diagnostic accuracy of methods to detect and diagnose caries in situ are particularly challenging. Low-certainty evidence suggests that imaging for the detection or diagnosis of early caries may have poor sensitivity but acceptable specificity, resulting in a relatively high number of false-negative results with the potential for early disease to progress. If left untreated, the opportunity to provide professional or self-care practices to arrest or reverse early caries lesions will be missed. The specificity of lesion detection is however relatively high, and one could argue that initiation of non-invasive management (such as the use of topical fluoride), is probably of low risk. CBCT showed superior sensitivity to analogue or digital radiographs but has very limited applicability to the general dental practitioner. However, given the high-radiation dose, and potential for caries-like artefacts from existing restorations, its use cannot be justified in routine caries detection. Nonetheless, if early incidental carious lesions are detected in CBCT scans taken for other purposes, these should be reported. CBCT has the potential to be used as a reference standard in diagnostic studies of this type. Despite the robust methodology applied in this comprehensive review, the results should be interpreted with some caution due to shortcomings in the design and execution of many of the included studies. Future research should evaluate the comparative accuracy of different methods, be undertaken in a clinical setting, and focus on minimising bias arising from the use of imperfect reference standards in clinical studies.
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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.