ArticleBMC oral health2022
Application of deep learning artificial intelligence technique to the classification of clinical orthodontic photos.
Article in BMC oral health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Clinical audit of an artificial intelligence (AI) empowered smile simulation system: a prospective clinical trial.Scientific reports · 2024Trial
- Artificial intelligence in orthodontics: clinical maturity, evidence gaps, and emerging applications-a narrative review.The Saudi dental journal · 2026Review
- Comparing Deep Learning Models for Identifying Maxillary Transverse Deficiency from Intraoral Photographs.International dental journal · 2026Article
- YOLO11-guided swin transformer for molar occlusion classification.Scientific reports · 2026Article
- Automatic estimation of single-tooth width from standardized two-dimensional occlusal photographs using deep learning.Head & face medicine · 2026Article
- Dental Crowding Categorization Network (DCC-Net): Explainable deep learning system for automatic categorization of dental crowding on intraoral photographs.Korean journal of orthodontics · 2026Article
- Automatic recognition of oral health status in mixed dentition via intraoral photography.BMC oral health · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Precision Assessment of Facial Asymmetry Using 3D Imaging and Artificial Intelligence.Journal of clinical medicine · 2025Article
- Diagnostic accuracy of generative large language artificial intelligence models for the assessment of dental crowding.BMC oral health · 2025Article
- Performance of ChatGPT-4 as an Auxiliary Tool: Evaluation of Accuracy and Repeatability on Orthodontic Radiology Questions.Bioengineering (Basel, Switzerland) · 2025Article
- Integration of artificial intelligence in orthodontic imaging: A bibliometric analysis of research trends and applications.Imaging science in dentistry · 2025Review
- Classification of Intraoral Photographs with Deep Learning Algorithms Trained According to Cephalometric Measurements.Diagnostics (Basel, Switzerland) · 2025Article
- Clinical Photography in Orthodontic Practice: Insights from a Nationwide Survey in Spain.Journal of clinical medicine · 2025Article
- AI for diagnosing malocclusions from 3D dental models.Bioinformation · 2025Article
- AI-driven dynamic orthodontic treatment management: personalized progress tracking and adjustments-a narrative review.Frontiers in dental medicine · 2025Review
- AI driven monitoring of orthodontic tooth movement using automated image analysis.Bioinformation · 2025Article
- A Deep Learning Framework for Automated Classification and Archiving of Orthodontic Diagnostic Documents.Cureus · 2024Article
- Profile Photograph Classification Performance of Deep Learning Algorithms Trained Using Cephalometric Measurements: A Preliminary Study.Diagnostics (Basel, Switzerland) · 2024Article
- Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTaking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the initial step while it was not easy for a machine to classify photos with a variety of facial and dental situations. This article presents a convolutional neural networks (CNNs) deep learning technique to classify orthodontic clinical photos according to their orientations.
methodsTo build an automated classification system, CNNs models of facial and intraoral categories were constructed, and the clinical photos that are routinely taken for orthodontic diagnosis were used to train the models with data augmentation. Prediction procedures were evaluated with separate photos whose purpose was only for prediction.
resultsOverall, a 98.0% valid prediction rate resulted for both facial and intraoral photo classification. The highest prediction rate was 100% for facial lateral profile, intraoral upper, and lower photos.
conclusionAn artificial intelligence system that utilizes deep learning with proper training models can successfully classify orthodontic facial and intraoral photos automatically. This technique can be used for the first step of a fully automated orthodontic diagnostic system in the future.
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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.