ArticleDiagnostics (Basel, Switzerland)2024
Caries Detection and Classification in Photographs Using an Artificial Intelligence-Based Model-An External Validation Study.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for binary dental caries diagnosis using intraoral images and dental radiographs: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- From Clinic to Community: An Interpretable Artificial Intelligence Framework for Enamel Caries Detection to Support Public Health Dentistry.European journal of dentistry · 2026Article
- From Algorithmic Performance to Clinical Translation: Translational Readiness of Imaging-Based Artificial Intelligence in Dentistry-A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Multi-Class Malocclusion Detection on Standardized Intraoral Photographs Using YOLOv11.Dentistry journal · 2026Article
- Annotated intraoral image dataset for dental caries detection.Scientific data · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveThis ex vivo diagnostic study aimed to externally validate a freely accessible AI-based model for caries detection, classification, localisation and segmentation using an independent image dataset. It was hypothesised that there would be no difference in diagnostic performance compared to previously published internal validation data.
methodsFor the independent dataset, 718 dental images representing different stages of carious (
resultsAn overall ACC of 92.0% was achieved for caries detection, with an ACC of 85.5-95.6%, SE of 42.9-93.3%, SP of 82.1-99.4% and AUC of 0.702-0.909 for the classification of caries. Furthermore, 97.0% of the cases were accurately localised. Fully and partially correct segmentation was achieved in 52.9% and 44.1% of the cases, respectively.
conclusionsThe validated AI-based model showed promising diagnostic performance in detecting and classifying caries using an independent image dataset. Future studies are needed to investigate the validity, reliability and practicability of AI-based models using dental photographs from different image sources and/or patient groups.
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Registered trials
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