Question · asked by 1 review
AI algorithms (predominantly CNN/deep learning) as index test vs Dental expert / visual-tactile clinical assessment (reference standard): Sensitivity
- Population
- Patients undergoing dental caries detection on dental images (teeth/surfaces across imaging modalities)
- Intervention
- AI algorithms (predominantly CNN/deep learning) as index test
- Comparison
- Dental expert / visual-tactile clinical assessment (reference standard)
- Outcome
- Sensitivity
AI algorithms have high pooled sensitivity (0.85) for detecting dental caries; ungraded, with very high heterogeneity (I2>95%) and evidence of publication bias.
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
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), Dental imaging: intraoral photographs, periapical, bitewing, panoramic radiographs, CBCT, near-infrared transillumination, Overall (all imaging modalities pooled) AI algorithms have high pooled sensitivity (0.85) for detecting dental caries; ungraded, with very high heterogeneity (I2>95%) and evidence of publication bias. Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of… (2025) All outcomes | Sensitivity 0.85 [0.83, 0.93] | 20 studies · 29423 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging modalities: An umbrella… 2025 All outcomes | October 15, 2024 | 20 | Not applicable |
Studies behind the findings
The primary studies the full-text reviews tie to this outcome. Reviews with headline outcomes only do not list them here.
- Recognition of Digital Dental X-ray Images Using a Convolutional Neural Network. · 2023 · included by 1 of these review
- An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images. · 2023 · included by 1 of these review
- Artificial intelligence for caries and periapical periodontitis detection · 2022 · included by 1 of these review
- Evaluation of a deep learning system for automatic detection of proximal surface dental caries on bitewing radiographs · 2022 · included by 1 of these review
- Dental Caries Detection Using Score-Based Multi-Input Deep Convolutional Neural Network · 2022 · included by 1 of these review
- Effectiveness of Using Radiology Images and Mask R-CNN for Stomatology · 2022 · included by 1 of these review
- Evaluation of Convolutional Neural Network for Automatic Caries Detection in Digital Radiograph Panoramic on Small Dataset · 2022 · included by 1 of these review
- A unified convolution neural network for dental caries classification · 2022 · included by 1 of these review
- Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs. · 2022 · included by 1 of these review
- Caries Detection on Intraoral Images Using Artificial Intelligence. · 2022 · included by 1 of these review
- Artificial Intelligence Application in Assessment of Panoramic Radiographs. · 2022 · included by 1 of these review
- Detection of Proximal Caries Lesions on Bitewing Radiographs Using Deep Learning Method. · 2022 · included by 1 of these review
- Caries detection with tooth surface segmentation on intraoral photographic images using deep learning. · 2022 · included by 1 of these review
- Automatic Feature Segmentation in Dental Periapical Radiographs. · 2022 · included by 1 of these review
- Development and Validation of a Cbct-Based Artificial Intelligence System for Accurate Diagnoses of Dental Diseases · 2021 · included by 1 of these review
- The Automatic Detection of Caries in Third Molars on Panoramic Radiographs Using Deep Learning: A Pilot Study · 2021 · included by 1 of these review
- Prediction of Radiation-Related Dental Caries Through PyRadiomics Features and Artificial Neural Network on Panoramic Radiography. · 2021 · included by 1 of these review
- Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks. · 2021 · included by 1 of these review
- The ADEPT study: a comparative study of dentists' ability to detect enamel-only proximal caries in bitewing radiographs with and without… · 2021 · included by 1 of these review
- A Smart Dental Health-IoT Platform Based on Intelligent Hardware, Deep Learning, and Mobile Terminal · 2020 · included by 1 of these review
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.