Same question across reviews · CL0076
DTA :: Caries detection/diagnosis -> Diagnostic accuracy
ProposalThis cluster groups questions by review type, intervention category and outcome domain. It is a proposal pending human validation, so questions in it may differ in population, comparator or timing.
Reviews
19
Questions
515
current versions
GRADE rated
47
Review overlap (CCA)
0.003
over all included studies of these reviews, not this question only
| Question | Effect | Evidence | Status / GRADE |
|---|---|---|---|
Quantitative light-induced fluorescence (QLF) vs Reference standard (ICDAS II visual examination, ADA classification, bitewing radiography) Occlusal caries detection: specificity · Patients (in vivo); permanent and primary teeth, occlusal surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.80 | 3 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Specificity · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Overall (all studies pooled) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | Proportion (weighted mean specificity, %) 87.90 | 7 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: sensitivity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.83 | 5 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology, radiography) Proximal caries detection: sensitivity · Extracted human teeth (in vitro); permanent teeth, proximal (approximal) surfaces · Overall (all studies permanent) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.83 | 3 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II) Proximal caries detection: specificity · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.81 | 3 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Diagnostic accuracy (prevalence of accuracy) · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Digital bitewing X-rays (Fig 4a) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | ~80 | 1 study | Ungraded |
AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard) Sensitivity · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Sensitivity 0.85 [0.83, 0.93] | 20 studies · 29423 participants | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: sensitivity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Primary dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.63 | 2 studies | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of proximal caries · human primary or permanent teeth with dental caries lesions · Permanent dentition (all 3 included studies) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.83 | 3 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: specificity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.66 | 3 studies | Ungraded |
Artificial intelligence algorithms for caries detection vs Dental expert or visual-tactile/clinical assessment as reference standard Negative post-test probability · Dental images or diagnostic tests for dental caries detection · Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Post-test probability 0.06 | 20 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (ICDAS II visual examination, ADA classification, bitewing radiography) Occlusal caries detection: sensitivity · Patients (in vivo); permanent and primary teeth, occlusal surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.86 | 4 studies | Ungraded |
AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard) Specificity · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Specificity 0.90 [0.85, 0.95] | 20 studies · 29423 participants | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of proximal caries · human primary or permanent teeth with dental caries lesions · permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.83 | 3 studies | Ungraded |
AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard) Area under the summary ROC curve · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | AUC 0.86 | 20 studies · 29423 participants | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (ICDAS II visual examination, ADA classification, bitewing radiography) Occlusal caries detection: sensitivity · Patients (in vivo); permanent and primary teeth, occlusal surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.86 | 3 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Diagnostic accuracy (prevalence of accuracy) · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Periapical X-rays (Fig 4c) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | ~76 | NR | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: specificity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.74 | 5 studies | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of occlusal caries · human primary or permanent teeth with dental caries lesions · permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.80 | 3 studies | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of proximal caries · human primary or permanent teeth with dental caries lesions · Permanent dentition (all 3 included studies) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.74 | 3 studies | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of proximal caries · human primary or permanent teeth with dental caries lesions · permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.74 | 3 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology, radiography) Proximal caries detection: specificity · Extracted human teeth (in vitro); permanent teeth, proximal (approximal) surfaces · Overall (all studies permanent) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.74 | 3 studies | Ungraded |
Artificial intelligence algorithms for caries detection vs Dental expert or visual-tactile/clinical assessment as reference standard Positive post-test probability · Dental images or diagnostic tests for dental caries detection · Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Post-test probability 0.79 | 20 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Sensitivity · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Overall (all studies pooled) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | OR 1.258 [0.493, 2.540] | 9 studies · 17190 participants | Ungraded |
quantitative light-induced fluorescence (QLF) vs reference standard detection of occlusal caries · human primary or permanent teeth with dental caries lesions · all dentitions Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.82 | 4 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II) Proximal caries detection: specificity · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.82 | 4 studies | Ungraded |
AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard) Positive likelihood ratio · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | LR+ 10.443 [7.505, 14.531] | 20 studies · 29423 participants | Ungraded |
AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard) Log diagnostic odds ratio · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | Log DOR 4.37 [3.16, 6.27] | 20 studies · 29423 participants | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II) Proximal caries detection: sensitivity · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.74 | 4 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: sensitivity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.91 | 3 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Diagnostic accuracy (prevalence of accuracy) · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Digital panoramic X-rays (Fig 4b) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | ~70 | 1 study | Ungraded |
AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard) Negative likelihood ratio · Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities) · Cross-sectional diagnostic accuracy (index test vs reference standard), Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | LR- 0.168 [0.138, 0.205] | 20 studies · 29423 participants | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (bitewing radiography, ADA classification, ICDAS II) Proximal caries detection: sensitivity · Patients (in vivo); permanent and primary teeth, proximal (approximal) surfaces · Permanent dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Sensitivity 0.76 | 3 studies | Ungraded |
AI systems for caries detection/segmentation on dental X-ray images (predominantly convolutional neural networks; also ANNs, DNNs, DCNN, FCNN, SVMs, decision trees, random forests; e.g. EfficientNet, ResNet, AlexNet) vs Human dentists / trained dental professionals or traditional radiographic interpretation (reference standard / reference models) Diagnostic accuracy (prevalence of accuracy) · Patients undergoing dental X-ray (radiographic) imaging for caries detection · Overall (all X-ray types pooled) Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis. (2025) | OR 2.72 [14.65, 14.65] | 14 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (histology; some ICDAS/radiography) Occlusal caries detection: specificity · Extracted human teeth (in vitro); permanent and primary teeth, occlusal surfaces · Primary dentition Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.81 | 2 studies | Ungraded |
Artificial intelligence algorithms for caries detection vs Dental expert or visual-tactile/clinical assessment as reference standard Diagnostic odds ratio · Dental images or diagnostic tests for dental caries detection · Overall (all imaging modalities pooled) Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) | DOR 70.9 [44.9, 111.9] | 20 studies | Ungraded |
Quantitative light-induced fluorescence (QLF) vs Reference standard (ICDAS II visual examination, ADA classification, bitewing radiography) Occlusal caries detection: specificity · Patients (in vivo); permanent and primary teeth, occlusal surfaces · Overall (all dentition) Diagnostic accuracy of quantitative light-induced fluorescence in detecting caries of various types and… (2025) | Specificity 0.82 | 4 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Intra-examiner reliability · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting intra-examiner reliability; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Intra-examiner reliability (kappa) reported in 6 studies, ranging from 0.52 to 1.00. No pooled estimate. | 6 studies | Ungraded |
Teledentistry diagnosis using DSLR or smartphone extra-oral photographs vs Visual or clinical examination Positive predictive value for dental caries diagnosis · Children and adults with primary or permanent dentition · Overall Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | no estimate extracted | 10 studies | Ungraded |
Teledentistry diagnosis using DSLR or smartphone extra-oral photographs vs Visual or clinical examination Negative predictive value for dental caries diagnosis · Children and adults with primary or permanent dentition · Overall Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | no estimate extracted | 10 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Overall diagnostic accuracy (agreement) · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting accuracy; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Accuracy 92.9% to 97.6% | 5 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Specificity · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting specificity; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Specificity ranged from 83% to 100% across studies (reported in 13 of 19 studies). No pooled estimate; meta-analysis not performed. | 13 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Positive predictive value · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting PPV; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | PPV 41.8% to 99% | 10 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Sensitivity · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting sensitivity; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Sensitivity Overall 48% to 98.3% across 13 studies; sensitivity is lesion-stage dependent: Kohara reported <40% for initial/moderate caries (75-100% for extensive) and early enamel lesions were less sensitive | 13 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Inter-examiner reliability · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting inter-examiner reliability; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Inter-examiner reliability (kappa) reported in 13 studies, ranging from 0.44 to 0.91. No pooled estimate. | 13 studies | Ungraded |
Teledentistry (index test): dental caries diagnosis from photographs taken with DSLR or smartphone cameras; store-and-forward vs Visual/clinical dental examination (reference standard) Negative predictive value · Children and adults with primary or permanent dentition undergoing dental caries assessment · Cross-sectional diagnostic accuracy (index test vs reference standard, in-vivo), Overall (studies reporting NPV; narrative synthesis) Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Negative predictive value reported in 10 of 19 studies; per-study values ranged from about 31% to 99.5% (Table 2). No pooled estimate. | 10 studies | Ungraded |
Teledentistry diagnosis using DSLR or smartphone extra-oral photographs vs Visual or clinical examination Sensitivity for dental caries diagnosis · Children and adults with primary or permanent dentition · Overall Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | Sensitivity Overall 48% to 98.3% across 13 studies; sensitivity is lesion-stage dependent: Kohara reported <40% for initial/moderate caries (75-100% for extensive) and early enamel lesions were less sensitive | 13 studies | Ungraded |
Teledentistry diagnosis using DSLR or smartphone extra-oral photographs vs Visual or clinical examination Specificity for dental caries diagnosis · Children and adults with primary or permanent dentition · Overall Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral… (2024) | no estimate extracted | 13 studies | Ungraded |
Visual or visual-tactile classification (e.g., ICDAS, ERK) vs Reference standard (histology, operative excavation, or enhanced visual examination) Sensitivity for detecting enamel caries · Asymptomatic children or adults suspected of enamel caries (clinical studies); extracted teeth (in vitro studies) · Analysis 1: fully within-person comparison (studies evaluating >=3 index tests) Enamel Caries Detection and Diagnosis: An Analysis of Systematic Reviews. (2022) | Sensitivity 0.82 [0.73, 0.89] | 19 studies · 2849 participants | Ungraded |
Machine-learning algorithms for dental caries detection vs Different machine-learning algorithms for caries prediction Caries detection accuracy on intraoral images · Dental images of human beings · Kühnisch et al., 2022; convolutional neural networks Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries. (2022) | Accuracy 93.3 | 1 study · 2417 participants | Ungraded |