ArticleJournal of dental research2019
Caries Detection with Near-Infrared Transillumination Using Deep Learning.
Article in Journal of dental research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 105 papers, 2 of them syntheses that pooled it.
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Who cites it
105 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review.BMC oral health · 2025Pooled it
- Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries.Journal of healthcare engineering · 2022Pooled it
- Portable Deep Learning-Assisted Device for Automated Interincisal Opening Assessment and Oral Submucous Fibrosis Lesion Identification: A Translational Clinical Study.Journal of imaging informatics in medicine · 2026Article
- A Genetic Algorithm-Optimized ConvNeXtV2-YOLOv8 Framework for Dental Caries Detection in Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Enhancing Dental Caries Classification with Adversarial Training on Bitewing Radiographs.Journal of imaging informatics in medicine · 2026Article
- Photonic Methods for the Assessment of Lesion Activity.Diagnostics (Basel, Switzerland) · 2026Review
- Evaluation of panoramic radiography for artificial intelligence-based assessment of impacted maxillary canines using cone-beam computed tomography as reference.BMC oral health · 2026Article
- Evaluating AI diagnostic accuracy in approximal dental caries detection on bitewing radiographs.Clinical oral investigations · 2026Article
- Automated Identification of Accessory Mental Foramen Using Cone-Beam Computed Tomography and Convolutional Neural Networks.International dental journal · 2026Article
- Deep Learning-Based Classification of Zirconia and Metal-Supported Porcelain Fixed Restorations on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Lightweight Deep Learning for Automated Dental Caries Screening from Pediatric Oral Photographs.Diagnostics (Basel, Switzerland) · 2026Article
- Assessing the Efficacy of Artificial Intelligence Platforms in Answering Dental Caries Multiple-Choice Questions: A Comparative Study of ChatGPT and Google Gemini Language Models.Dentistry journal · 2026Article
- Attitudes and perceptions of dental students and interns toward AI in dentistry: a cross-sectional survey in a Saudi population.BMC medical education · 2026Article
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- Accuracy of deep learning-based AI models for early caries lesion detection: the influence of annotation quality and reference choice.Clinical oral investigations · 2025Article
- Novel metaheuristic optimized latent diffusion framework for automated oral disease detection in public health screening.Scientific reports · 2025Article
- Tooth-to-white spot lesion YOLO: a novel model for white spot lesion detection.BMC oral health · 2025Article
- Enhanced diagnostic pipeline for maxillary sinus-maxillary molars relationships: a novel implementation of Detectron2 with faster R-CNN R50 FPN 3x on CBCT images.BMC oral health · 2025Article
- Modular Deep-Learning Pipelines for Dental Caries Data Streams: A Twin-Cohort Proof-of-Concept.Dentistry journal · 2025Article
- Embracing Artificial Intelligence in Dental Practice: An Exploratory Study of Romanian Clinicians' Perspectives and Experiences.Dentistry journal · 2025Article
45 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dental caries is the most prevalent chronic condition worldwide. Early detection can significantly improve treatment outcomes and reduce the need for invasive procedures. Recently, near-infrared transillumination (TI) imaging has been shown to be effective for the detection of early stage lesions. In this work, we present a deep learning model for the automated detection and localization of dental lesions in TI images. Our method is based on a convolutional neural network (CNN) trained on a semantic segmentation task. We use various strategies to mitigate issues related to training data scarcity, class imbalance, and overfitting. With only 185 training samples, our model achieved an overall mean intersection-over-union (IOU) score of 72.7% on a 5-class segmentation task and specifically an IOU score of 49.5% and 49.0% for proximal and occlusal carious lesions, respectively. In addition, we constructed a simplified task, in which regions of interest were evaluated for the binary presence or absence of carious lesions. For this task, our model achieved an area under the receiver operating characteristic curve of 83.6% and 85.6% for occlusal and proximal lesions, respectively. Our work demonstrates that a deep learning approach for the analysis of dental images holds promise for increasing the speed and accuracy of caries detection, supporting the diagnoses of dental practitioners, and improving patient outcomes.
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