ArticleBioengineering (Basel, Switzerland)2023
Deep Learning-Based Recognition of Periodontitis and Dental Caries in Dental X-ray Images.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 4 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Diagnostic performance of artificial intelligence in periapical radiography: a systematic review.Odontology · 2026Pooled it
- An interdisciplinary framework for artificial intelligence, precision medicine, and ethical governance in periodontal care: a systematic review.BMC oral health · 2026Pooled it
- Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.BMC oral health · 2025Pooled it
- Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis.Head & face medicine · 2025Pooled it
- A Network-Medicine Framework for Intra-Oral Comorbidity: Age-Stratified Clustering and Quasi-Causal Progression Modeling from Outpatient Electronic Health Records.Bioengineering (Basel, Switzerland) · 2026Article
- Structure-Guided Tooth Numbering and Lesion Localization in Visible Light Oral Images.Journal of imaging · 2026Article
- EnamelNet-TRiX: A Lesion-Aware Dual-Transformer With Cross-Attention for Early and Advanced Enamel Caries Diagnosis.International dental journal · 2026Article
- MultiDentNet: a unified deep learning framework for multi-class dental condition screening and preliminary oral lesion triage.Scientific reports · 2026Article
- Research on the efficacy of hybrid deep learning models for image-based classification of common oral conditions.BMC oral health · 2026Article
- OralSegNet: An Approach to Early Detection of Oral Disease Using Transfer Learning.Oral diseases · 2026Article
- Evaluating the Test Characteristics of a Prototype for AI-Assisted Radiographic Detection.Dentistry journal · 2026Article
- Thermography and Infrared Spectroscopy in the Detection of Periodontal Inflammation In Vivo: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Multi-label diagnosis of dental conditions from panoramic x-rays using attention-enhanced deep learning.Oral and maxillofacial surgery · 2025Article
- Detection of dental caries under fixed dental prostheses by analyzing digital panoramic radiographs with artificial intelligence algorithms based on deep learning methods.BMC oral health · 2025Article
- Hybrid CNN-Transformer Model for Accurate Impacted Tooth Detection in Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Optimal Training Positive Sample Size Determination for Deep Learning with a Validation on CBCT Image Caries Recognition.Diagnostics (Basel, Switzerland) · 2024Article
- Toward Digital Periodontal Health: Recent Advances and Future Perspectives.Bioengineering (Basel, Switzerland) · 2024Review
- Advances in hybridized nanoarchitectures for improved oro-dental health.Journal of nanobiotechnology · 2024Review
- An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network.Journal of imaging informatics in medicine · 2024Article
- The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?Molecules (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Dental X-ray images are important and useful for dentists to diagnose dental diseases. Utilizing deep learning in dental X-ray images can help dentists quickly and accurately identify common dental diseases such as periodontitis and dental caries. This paper applies image processing and deep learning technologies to dental X-ray images to propose a simultaneous recognition method for periodontitis and dental caries. The single-tooth X-ray image is detected by the YOLOv7 object detection technique and cropped from the periapical X-ray image. Then, it is processed through contrast-limited adaptive histogram equalization to enhance the local contrast, and bilateral filtering to eliminate noise while preserving the edge. The deep learning architecture for classification comprises a pre-trained EfficientNet-B0 and fully connected layers that output two labels by the sigmoid activation function for the classification task. The average precision of tooth detection using YOLOv7 is 97.1%. For the recognition of periodontitis, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve is 98.67%, and the AUC of the precision-recall (PR) curve is 98.38%. For the recognition of dental caries, the AUC of the ROC curve is 98.31%, and the AUC of the PR curve is 97.55%. Different from the conventional deep learning-based methods for a single disease such as periodontitis or dental caries, the proposed approach can provide the recognition of both periodontitis and dental caries simultaneously. This recognition method presents good performance in the identification of periodontitis and dental caries, thus facilitating dental diagnosis.
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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.