ArticleDento maxillo facial radiology2020
Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network.
Article in Dento maxillo facial radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 3 of them syntheses that pooled it.
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Who cites it
79 citing papers in PubMed, 3 syntheses or guidelines pooled it, 155 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence-based deep learning models in detecting furcation involvement: A systematic review and meta-analysis.Journal of periodontology · 2026Pooled it
- Evaluating YOLO for dental caries diagnosis: a systematic review and meta-analysis.Evidence-based dentistry · 2025Pooled it
- Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review.Pain research & management · 2021Pooled it
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Effectiveness of Artificial Intelligence in the Automatic Diagnosis of Lesions Detected in Panoramic Radiographs.Journal of imaging informatics in medicine · 2026Article
- Weighted knowledge distillation for semi-supervised segmentation of maxillary sinus in panoramic X-ray images.Scientific reports · 2026Article
- Detection of Jaw Lesions on Panoramic Radiographs Using Deep Learning Method.Journal of imaging informatics in medicine · 2026Article
- Automated detection and classification of maxillary sinus variations using slice-based and full-volume CBCT deep learning models.BMC oral health · 2026Article
- Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with a Structured Output (SLSO) Framework.Diagnostics (Basel, Switzerland) · 2026Article
- U-net-based segmentation of foreign bodies and ghost images in panoramic radiographs.Oral radiology · 2026Article
- Automated Identification of Accessory Mental Foramen Using Cone-Beam Computed Tomography and Convolutional Neural Networks.International dental journal · 2026Article
- Artificial Intelligence in the Diagnosis of Odontogenous Cysts and Ameloblastomas-A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- Deep learning-based approach for differential diagnosis of odontogenic cysts from histopathological images.Medicina oral, patologia oral y cirugia bucal · 2026Article
- Segmentation-Guided Preprocessing Improves Deep Learning Diagnostic Accuracy and Confidence of Ameloblastoma and Odontogenic Keratocyst in Cone Beam CT Images-A Preliminary Study.Diagnostics (Basel, Switzerland) · 2026Article
- From Image to Diagnosis: Convolutional Neural Networks in Tongue Lesions.Journal of imaging informatics in medicine · 2026Article
- How artificial intelligence is reshaping citation impact in oral and maxillofacial radiology journals: an 8-year analysis with editorial and clinical implications (JCR 2017-2024).Frontiers in research metrics and analytics · 2026Article
- Artificial intelligence-assisted decision-making in third molar assessment using ChatGPT: is it really a valid tool?Oral radiology · 2026Article
- The Leeuwenhoek paradox in dental artificial intelligence: A narrative review of artificial intelligence-detected subclinical periapical changes and human-labeled false positives.Journal of dental sciences · 2026Review
- Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review.European endodontic journal · 2025Review
- Deep learning for imaging diagnosis of jaw cystic lesions and maxillofacial tumors: A narrative review.The Journal of international medical research · 2025Review
19 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe purpose of this study was to automatically diagnose odontogenic cysts and tumors of both jaws on panoramic radiographs using deep learning. We proposed a novel framework of deep convolution neural network (CNN) with data augmentation for detection and classification of the multiple diseases.
methodsWe developed a deep CNN modified from YOLOv3 for detecting and classifying odontogenic cysts and tumors of both jaws. Our data set of 1282 panoramic radiographs comprised 350 dentigerous cysts (DCs), 302 periapical cysts (PCs), 300 odontogenic keratocysts (OKCs), 230 ameloblastomas (ABs), and 100 normal jaws with no disease. In addition, the number of radiographs was augmented 12-fold by flip, rotation, and intensity changes. We evaluated the classification performance of the developed CNN by calculating sensitivity, specificity, accuracy, and area under the curve (AUC) for diseases of both jaws.
resultsThe overall classification performance for the diseases improved from 78.2% sensitivity, 93.9% specificity,91.3% accuracy, and 0.86 AUC using the CNN with unaugmented data set to 88.9% sensitivity, 97.2% specificity, 95.6% accuracy, and 0.94 AUC using the CNN with augmented data set. CNN using augmented data set had the following sensitivities, specificities, accuracies, and AUCs: 91.4%, 99.2%, 97.8%, and 0.96 for DCs, 82.8%, 99.2%, 96.2%, and 0.92 for PCs, 98.4%,92.3%,94.0%, and 0.97 for OKCs, 71.7%, 100%, 94.3%, and 0.86 for ABs, and 100.0%, 95.1%, 96.0%, and 0.97 for normal jaws, respectively.
conclusionThe CNN method we developed for automatically diagnosing odontogenic cysts and tumors of both jaws on panoramic radiographs using data augmentation showed high sensitivity, specificity, accuracy, and AUC despite the limited number of panoramic images involved.
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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.