ArticleNeural computing & applications2022
CariesNet: a deep learning approach for segmentation of multi-stage caries lesion from oral panoramic X-ray image.
Article in Neural computing & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 4 syntheses or guidelines pooled it, 131 citations in OpenAlex.
- Artificial intelligence for binary dental caries diagnosis using intraoral images and dental radiographs: a systematic review and meta-analysis.BMC oral health · 2026Pooled it
- Accuracy of artificial intelligence in caries detection: a systematic review and meta-analysis.Head & face medicine · 2025Pooled it
- Panoramic imaging errors in machine learning model development: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.BMC oral health · 2024Pooled it
- Multicentre Evaluation of AI-Assisted Caries Detection on Panoramic Radiographs Among Early-Career Dentists.International dental journal · 2026Article
- YOLOv8m-segmentation for detecting cervical burnout and caries in bitewing radiographs: A deep learning approach.Imaging science in dentistry · 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
- A novel Pulp Caries GAN multi loss GAN with new pulp inspired metaheuristics for pediatric dental caries detection and segmentation.Scientific reports · 2026Article
- Bio-inspired neutrosophic-enzyme intelligence framework for pediatric dental disease detection using multi-modal clinical data.Scientific reports · 2025Article
- Clinical application of deep learning for enhanced multistage caries detection in panoramic radiographs.Scientific reports · 2025Article
- Binary Classification of Pneumonia in Chest X-Ray Images Using Modified Contrast-Limited Adaptive Histogram Equalization Algorithm.Sensors (Basel, Switzerland) · 2025Article
- Artificial intelligence (AI) in restorative dentistry: current trends and future prospects.BMC oral health · 2025Review
- Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
- Current AI Applications and Challenges in Oral Pathology.Oral (Basel, Switzerland) · 2025Article
- Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging.Scientific reports · 2025Article
- DeepLabv3 + method for detecting and segmenting apical lesions on panoramic radiography.Clinical oral investigations · 2025Article
- Accuracy of AI-based binary classification for detecting malocclusion in the mixed dentition stage.PloS one · 2025Article
- Deep Learning-Based Algorithm for Staging Secondary Caries in Bitewings.Caries research · 2025Article
- Embracing technological revolution: A panorama of machine learning in dentistry.Medicina oral, patologia oral y cirugia bucal · 2024Review
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dental caries has been a common health issue throughout the world, which can even lead to dental pulp and root apical inflammation eventually. Timely and effective treatment of dental caries is vital for patients to reduce pain. Traditional caries disease diagnosis methods like naked-eye detection and panoramic radiograph examinations rely on experienced doctors, which may cause misdiagnosis and high time-consuming. To this end, we propose a novel deep learning architecture called CariesNet to delineate different caries degrees from panoramic radiographs. We firstly collect a high-quality panoramic radiograph dataset with 3127 well-delineated caries lesions, including shallow caries, moderate caries, and deep caries. Then we construct CariesNet as a U-shape network with the additional full-scale axial attention module to segment these three caries types from the oral panoramic images. Moreover, we test the segmentation performance between CariesNet and other baseline methods. Experiments show that our method can achieve a mean 93.64% Dice coefficient and 93.61% accuracy in the segmentation of three different levels of caries.
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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.