ArticleClinical oral investigations2022
Artificial intelligence-based diagnostics of molar-incisor-hypomineralization (MIH) on intraoral photographs.
Article in Clinical oral investigations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
- Comparative assessment of quality, consistency, and reference accuracy of MIH-related clinical information generated by ChatGPT-4o and DeepSeek R1.BMC oral health · 2026Article
- Application of Artificial Intelligence in Detecting Dental Anomalies: Current Models, Imaging Modalities, and Future Directions.Health science reports · 2026Article
- Artificial Intelligence in Pediatric Dentistry: A Systematic Review and Meta-Analysis.Children (Basel, Switzerland) · 2026Review
- Assessment of Apical Patency in Permanent First Molars Using Deep Learning on CBCT-Derived Pseudopanoramic Images: A Retrospective Study.Bioengineering (Basel, Switzerland) · 2025Article
- Automatic recognition of oral health status in mixed dentition via intraoral photography.BMC oral health · 2025Article
- Classification of Intraoral Photographs with Deep Learning Algorithms Trained According to Cephalometric Measurements.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- 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
- Is Italian Dentists' Knowledge of Enamel Development Defects Adequate? A Nationwide Survey.International dental journal · 2024Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study.BMC oral health · 2024Article
- Multi-Quantifying Maxillofacial Traits via a Demographic Parity-Based AI Model.BME frontiers · 2024Article
- Automatized Detection of Periodontal Bone Loss on Periapical Radiographs by Vision Transformer Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Detection and localization of caries and hypomineralization on dental photographs with a vision transformer model.NPJ digital medicine · 2023Article
- Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this study was to develop and validate a deep learning-based convolutional neural network (CNN) for the automated detection and categorization of teeth affected by molar-incisor-hypomineralization (MIH) on intraoral photographs. MATERIALS AND
methodsThe data set consisted of 3241 intraoral images (767 teeth with no MIH/no intervention, 76 with no MIH/atypical restoration, 742 with no MIH/sealant, 815 with demarcated opacity/no intervention, 158 with demarcated opacity/atypical restoration, 181 with demarcated opacity/sealant, 290 with enamel breakdown/no intervention, 169 with enamel breakdown/atypical restoration, and 43 with enamel breakdown/sealant). These images were divided into a training (N = 2596) and a test sample (N = 649). All images were evaluated by an expert group, and each diagnosis served as a reference standard for cyclic training and evaluation of the CNN (ResNeXt-101-32 × 8d). Statistical analysis included the calculation of contingency tables, areas under the receiver operating characteristic curve (AUCs) and saliency maps.
resultsThe developed CNN was able to categorize teeth with MIH correctly with an overall diagnostic accuracy of 95.2%. The overall SE and SP amounted to 78.6% and 97.3%, respectively, which indicate that the CNN performed better in healthy teeth compared to those with MIH. The AUC values ranging from 0.873 (enamel breakdown/sealant) to 0.994 (atypical restoration/no MIH).
conclusionIt was possible to categorize the majority of clinical photographs automatically by using a trained deep learning-based CNN with an acceptably high diagnostic accuracy. CLINICAL RELEVANCE: Artificial intelligence-based dental diagnostics may support dental diagnostics in the future regardless of the need to improve accuracy.
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