ArticleScientific reports2021
Dental enumeration and multiple treatment detection on panoramic X-rays using deep learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 6 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Artificial Intelligence-assisted Diagnosis of Carotid Artery Calcifications on Panoramic Radiographs: A Meta-analysis.International dental journal · 2026Pooled it
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Exploring a decade of deep learning in dentistry: A comprehensive mapping review.Clinical oral investigations · 2025Pooled it
- Application and performance of artificial intelligence-based models in the detection, segmentation and classification of periapical lesions: a systematic review.Frontiers in dental medicine · 2025Pooled it
- Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.International dental journal · 2024Pooled it
- Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.Dento maxillo facial radiology · 2024Pooled it
- Automated tooth numbering on panoramic radiographs versus cone-beam computed tomographs: a diagnostic accuracy study of a commercial artificial intelligence system.Dento maxillo facial radiology · 2026Article
- Tooth X-Ray Image Segmentation Based on ResU-Net with Coordinate Attention and Boundary-Aware Mechanisms.Sensors (Basel, Switzerland) · 2026Article
- A Hybrid Deep Learning Framework for Automated Dental Disorder Diagnosis from X-Ray Images.Journal of clinical medicine · 2026Article
- CaliDent-Net: domain-constrained self-supervised pre-training with parallel attention and prototype calibration for dental radiograph analysis.Frontiers in medicine · 2026Article
- The Influence of Examiner Experience on Inter- and Intra-Rater Agreement in Dental Restorations Identification on Three-Dimensional Digital Models.Medicina (Kaunas, Lithuania) · 2025Article
- AI-powered precision in dental radiographic analysis using tailored CNNs for tooth numbering and cavity detection.PLOS digital health · 2025Article
- Comparative analysis of YOLO variants for dental implant brand identification.Scientific reports · 2025Article
- Detection and dual-label classification of tooth number and condition in dental panoramic radiographs including deciduous teeth.Radiological physics and technology · 2025Article
- A hybrid convolutional neural network model for dental age estimation using buccal alveolar bone level for Saudi children.BMC oral health · 2025Article
- Estimating Molar Root Volume from Panoramic Radiographs Using a Geometric Approach-An Experimental Method Comparison.Medicina (Kaunas, Lithuania) · 2025Article
- Detection of Fractured Endodontic Instruments in Periapical Radiographs: A Comparative Study of YOLOv8 and Mask R-CNN.Diagnostics (Basel, Switzerland) · 2025Article
- Accuracy of Artificial Intelligence Models in Detecting Peri-Implant Bone Loss: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Accuracy of the Hounsfield Unit Values Measured by Implant Planning Software.Dentistry journal · 2024Article
- Enhanced Panoramic Radiograph-Based Tooth Segmentation and Identification Using an Attention Gate-Based Encoder-Decoder Network.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
In this paper, a new powerful deep learning framework, named as DENTECT, is developed in order to instantly detect five different dental treatment approaches and simultaneously number the dentition based on the FDI notation on panoramic X-ray images. This makes DENTECT the first system that focuses on identification of multiple dental treatments; namely periapical lesion therapy, fillings, root canal treatment (RCT), surgical extraction, and conventional extraction all of which are accurately located within their corresponding borders and tooth numbers. Although DENTECT is trained on only 1005 images, the annotations supplied by experts provide satisfactory results for both treatment and enumeration detection. This framework carries out enumeration with an average precision (AP) score of 89.4% and performs treatment identification with a 59.0% AP score. Clinically, DENTECT is a practical and adoptable tool that accelerates the process of treatment planning with a level of accuracy which could compete with that of dental clinicians.
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