ArticleForensic sciences research2022
Automatic detection of teeth and dental treatment patterns on dental panoramic radiographs using deep neural networks.
Article in Forensic sciences research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- Artificial intelligence in forensic science: a systematic review. Part I: personal identification.International journal of legal medicine · 2026Pooled it
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.International dental journal · 2024Pooled it
- Deep Learning-Based Panoramic Radiograph Retrieval from Antemortem Images for Forensic Identification.International journal of legal medicine · 2026Article
- Development of an automated YOLO v8-based deep learning teeth numbering model for digital orthopantomogram.Oral radiology · 2026Article
- An augmentation-enhanced transformer model for forensic identification using panoramic dental x-rays.International journal of legal medicine · 2026Article
- Automated forensic human identification on dental panoramic radiographs using transformer-based detection with supervised metric learning.BMC oral health · 2026Article
- Decision-Support for Restorative Dentistry: Hybrid Optimization Enhances Detection on Panoramic Radiographs.Healthcare (Basel, Switzerland) · 2025Article
- Evaluation of root canal filling length on periapical radiograph using artificial intelligence.Oral radiology · 2025Article
- Artificial Intelligence in Dentistry: A Descriptive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial intelligence application in endodontics: A narrative review.Imaging science in dentistry · 2024Review
- Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs.Bioengineering (Basel, Switzerland) · 2024Article
- A diagnostic strategy for pulmonary fat embolism based on routine H&E staining using computational pathology.International journal of legal medicine · 2024Article
- Can deep learning identify humans by automatically constructing a database with dental panoramic radiographs?PloS one · 2024Article
- Convolutional neural networks for automated tooth numbering on panoramic radiographs: A scoping review.Imaging science in dentistry · 2023Article
- Artificial intelligence in forensic medicine and forensic dentistry.The Journal of forensic odonto-stomatology · 2023Review
- Development of deep learning model and evaluation in real clinical practice of lingual mandibular bone depression (Stafne cyst) on panoramic radiographs.Dento maxillo facial radiology · 2023Article
- A deep learning-based automatic tool for measuring the lengths of linear scars: forensic applications.Forensic sciences research · 2023Article
- Automatic diagnosis of retention pseudocyst in the maxillary sinus on panoramic radiographs using a convolutional neural network algorithm.Scientific reports · 2023Article
- Applications of contemporary artificial intelligence technology in forensic odontology as primary forensic identifier: A scoping review.Frontiers in artificial intelligence · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disaster victim identification issues are especially critical and urgent after a large-scale disaster. The aim of this study was to suggest an automatic detection of natural teeth and dental treatment patterns based on dental panoramic radiographs (DPRs) using deep learning to promote its applicability as human identifiers. A total of 1 638 DPRs, of which the chronological age ranged from 20 to 49 years old, were collected from January 2000 to November 2020. This dataset consisted of natural teeth, prostheses, teeth with root canal treatment, and implants. The detection of natural teeth and dental treatment patterns including the identification of teeth number was done with a pre-trained object detection network which was a convolutional neural network modified by EfficientDet-D3. The objective metrics for the average precision were 99.1% for natural teeth, 80.6% for prostheses, 81.2% for treated root canals, and 96.8% for implants, respectively. The values for the average recall were 99.6%, 84.3%, 89.2%, and 98.1%, in the same order, respectively. This study showed outstanding performance of convolutional neural network using dental panoramic radiographs in automatically identifying teeth number and detecting natural teeth, prostheses, treated root canals, and implants.
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