ArticleDiagnostics (Basel, Switzerland)2023
A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs.
Article in Diagnostics (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, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.Clinical and experimental dental research · 2024Pooled it
- A Deep Learning Model for Second-Molar Lesions Related to Impacted Third Molars.International dental journal · 2026Article
- Deep Learning-Based Dental Caries Diagnosis on Panoramic Radiographies: Performance of YOLOv8 Versus Human Observers.Diagnostics (Basel, Switzerland) · 2026Article
- Automated differentiation of caries requiring filling and caries necessitating root canal treatment using machine learning.Oral radiology · 2026Article
- Clinical application of deep learning for enhanced multistage caries detection in panoramic radiographs.Scientific reports · 2025Article
- Charting New Territory: AI Applications in Dental Caries Detection from Panoramic Imaging.Dentistry journal · 2025Review
- Automated detection and classification of osteolytic lesions in panoramic radiographs using CNNs and vision transformers.BMC oral health · 2025Article
- Patients' attitudes toward artificial intelligence in dentistry and their trust in dentists.Oral radiology · 2025Article
- Using AI in Optimizing Oral and Dental Diagnoses-A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- A hybrid approach based on multipath Swin transformer and ConvMixer for white blood cells classification.Health information science and systems · 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
- Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligent-driven decision-making for automating root fracture detection in periapical radiographs.BDJ open · 2024Article
- Optimal Training Positive Sample Size Determination for Deep Learning with a Validation on CBCT Image Caries Recognition.Diagnostics (Basel, Switzerland) · 2024Article
- The Use of Artificial Intelligence in Caries Detection: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs.Dento maxillo facial radiology · 2024Article
- Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks' ensemble.Heliyon · 2024Article
- Dental Caries Detection and Classification in CBCT Images Using Deep Learning.International dental journal · 2024Article
- Combining public datasets for automated tooth assessment in panoramic radiographs.BMC oral health · 2024Article
- Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry's Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success.
Indexed as
Identifiers
What OpenQuestion holds
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.