SynthesisEvidence-based dentistry2025
Applications of AI-based deep learning models for detecting dental caries on intraoral images - a systematic review.
Synthesis in Evidence-based dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
What it found
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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
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Accuracy of Deep Learning Models in Detecting Peri-Implant Marginal Bone Loss: A Systematic Review and Meta-Analysis.Clinical oral implants research · 2026Pooled it
- Evaluating YOLO for dental caries diagnosis: a systematic review and meta-analysis.Evidence-based dentistry · 2025Pooled it
- Accuracy of teledentistry in detecting dental caries among school children in Makkah City: a diagnostic comparative study.BMC oral health · 2026Article
- Enhancing deep learning-based dental caries detection through feature pyramid network-based image segmentation.Journal of conservative dentistry and endodontics · 2026Article
- Deep learning models for the detection of dental-findings and tooth-types using video data.BMC oral health · 2026Article
- Assessing diagnostic performance of multimodal LLMs and a custom convolutional neural network in tooth-level caries detection and localization.BMC oral health · 2026Article
- Detection of Pediatric Dental Caries in Panoramic Radiograph Using Deep Learning: A Benchmark Study on MD-OPG.Sensors (Basel, Switzerland) · 2026Article
- Analytical validation of Smilo.ai: evaluating an AI-driven platform for automated oral health screening and global diagnostic accessibility.Frontiers in oral health · 2026Article
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- Annotated intraoral image dataset for dental caries detection.Scientific data · 2025Article
- PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs.BMC oral health · 2025Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis systematic review aimed to assess the effectiveness of Artificial Intelligence (AI)-based Deep Learning (DL) models in the detection of dental caries on intraoral images.
methodsThis systematic review adhered to PRISMA 2020 guidelines conducting an electronic search on PubMed, Scopus, and CENTRAL databases for retrospective, prospective, and cross-sectional studies published till 1st June 2024. Methodological and performance metrics of clinical studies utilizing DL models were assessed. A modified QUADAS risk of bias tool was used for quality assessment.
resultsOut of 273 studies identified, a total of 23 were included with 19 studies having a low risk and 4 studies having a high risk of bias. Overall accuracy ranged from 56% to 99.1%, sensitivity ranged from 23% to 98% and specificity ranged from 65.7% to 100%. Only 3 studies utilized explainable AI (XAI) techniques for caries detection. A total of 4 studies exhibited a level 4 deployment status by developing mobile or web-based applications.
conclusionAI-based DL models have demonstrated promising prospects in enhancing the detection of dental caries, especially in terms of low-resource settings. However, there is a need for future deployed studies to enhance the AI models to improve their real-world applications.
Indexed as
Identifiers
39609513What OpenQuestion holds
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.