ArticleHeliyon2024
Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks' ensemble.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep convolutional neural networks for early detection of interproximal caries using bitewing radiographs: A systematic review.Evidence-based dentistry · 2025Pooled it
- A systematic literature review: exploring the challenges of ensemble model for medical imaging.BMC medical imaging · 2025Pooled it
- Caries is a gradient, not a boundary: Detection rather than segmentation is the appropriate deep learning approach.Imaging science in dentistry · 2025Article
- Tooth-level detection and mapping of dental pathologies on panoramic radiographs using YOLOv11 and RT-DETR.MethodsX · 2025Article
- Hierarchical attention mechanism combined with deep neural networks for accurate semantic segmentation of dental structures in panoramic radiographs.Scientific reports · 2025Article
- [An lightweight algorithm for multi-dimensional optimization of intelligent detection of dental abnormalities on panoramic oral X-ray images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Charting New Territory: AI Applications in Dental Caries Detection from Panoramic Imaging.Dentistry journal · 2025Review
- PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs.BMC oral health · 2025Article
- DeepLabv3 + method for detecting and segmenting apical lesions on panoramic radiography.Clinical oral investigations · 2025Article
- Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Background: Dental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy. Method: The first model of CariSeg, trained using the U-Net architecture, segments the area of interest, the teeth, and crops the radiograph around it. The next component segments the carious lesions and it is an ensemble composed of three architectures: U-Net, Feature Pyramid Network, and DeeplabV3. For tooth identification two merged datasets were used: The Tufts Dental Database consisting of 1000 panoramic radiography images and another dataset of 116 anonymized panoramic X-rays, taken at Noor Medical Imaging Center, Qom. For carious lesion segmentation, a dataset consisting of 150 panoramic X-ray images was acquired from the Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca. Results: The experiments demonstrate that our approach results in 99.42% accuracy and a mean 68.2% Dice coefficient. Conclusions: AI helps in detecting carious lesions by analyzing dental X-rays and identifying cavities that might be missed by human observers, leading to earlier detection and treatment of cavities and resulting in better oral health outcomes.
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