ArticleNPJ digital medicine2023
Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Which diagnostic tools and criteria to use for diagnosis of HSPM and MIH? A systematic review.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2026Pooled it
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- DentSeg3: A Three-Branch Specialized Fusion Network for Dental Lesion Segmentation in Color Images.Journal of imaging informatics in medicine · 2026Article
- Diagnostic Performance of Artificial Intelligence Models for Periodontitis Disease Detection Using Panoramic Radiographs: A Systematic Review.Dentistry journal · 2026Review
- Enhancing YOLOv8s with MHSA and GA for robust caries detection on periapical radiographs.BMC oral health · 2026Article
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
- Artificial intelligence in dentistry: Training and testing a caries detection model with intraoral scans.Journal of conservative dentistry and endodontics · 2026Article
- Artificial Intelligence in Pediatric Dentistry: A Systematic Review and Meta-Analysis.Children (Basel, Switzerland) · 2026Review
- Accuracy of artificial intelligence applications in periodontics: a thematic narrative review.Frontiers in dental medicine · 2026Review
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- CBMNet: a dual-attention enhanced ConvNeXt model for accurate G.V. Black type I-III classification in intraoral periapical radiographs.Scientific reports · 2025Article
- Diagnostic accuracy of generative large language artificial intelligence models for the assessment of dental crowding.BMC oral health · 2025Article
- Evaluation of Apical Closure in Panoramic Radiographs Using Vision Transformer Architectures ViT-Based Apical Closure Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Toward artificial intelligence in dental prosthesis planning - a preliminary in-silico feasibility study.BMC oral health · 2025Article
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- Calibration of SB Brasil 2023 examiners: use of technologies associated with the In-Lux method.Brazilian oral research · 2025Article
- Application of machine learning in dentistry: insights, prospects and challenges.Acta odontologica Scandinavica · 2025Review
- Early detection of MIH in children by using artificial intelligence.European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2024Article
- Caries Detection and Classification in Photographs Using an Artificial Intelligence-Based Model-An External Validation Study.Diagnostics (Basel, Switzerland) · 2024Article
- Trustworthy Artificial Intelligence in Dentistry: Learnings from the EU AI Act.Journal of dental research · 2024Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Caries and molar-incisor hypomineralization (MIH) are among the most prevalent diseases worldwide and need to be reliably diagnosed. The use of dental photographs and artificial intelligence (AI) methods may potentially contribute to realizing accurate and automated diagnostic visual examinations in the future. Therefore, the present study aimed to develop an AI-based algorithm that can detect, classify and localize caries and MIH. This study included an image set of 18,179 anonymous photographs. Pixelwise image labeling was achieved by trained and calibrated annotators using the Computer Vision Annotation Tool (CVAT). All annotations were made according to standard methods and were independently checked by an experienced dentist. The entire image set was divided into training (N = 16,679), validation (N = 500) and test sets (N = 1000). The AI-based algorithm was trained and finetuned over 250 epochs by using image augmentation and adapting a vision transformer network (SegFormer-B5). Statistics included the determination of the intersection over union (IoU), average precision (AP) and accuracy (ACC). The overall diagnostic performance in terms of IoU, AP and ACC were 0.959, 0.977 and 0.978 for the finetuned model, respectively. The corresponding data for the most relevant caries classes of non-cavitations (0.630, 0.813 and 0.990) and dentin cavities (0.692, 0.830, and 0.997) were found to be high. MIH-related demarcated opacity (0.672, 0.827, and 0.993) and atypical restoration (0.829, 0.902, and 0.999) showed similar results. Here, we report that the model achieves excellent precision for pixelwise detection and localization of caries and MIH. Nevertheless, the model needs to be further improved and externally validated.
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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.