ArticleDento maxillo facial radiology2021
Computer tomographic differential diagnosis of ameloblastoma and odontogenic keratocyst: classification using a convolutional neural network.
Article in Dento maxillo facial radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Diagnostic Accuracy of Artificial Intelligence Models for Detecting Odontogenic Keratocytes on CBCT Imaging: A Systematic Review and Meta-Analysis.Health science reports · 2026Review
- Detection of Jaw Lesions on Panoramic Radiographs Using Deep Learning Method.Journal of imaging informatics in medicine · 2026Article
- A deep learning approach for the diagnosis and recurrence prediction of OKC.Scientific reports · 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
- Machine learning in dentistry and oral surgery: charting the course with bibliometric insights.Head & face medicine · 2025Article
- Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta-Analysis.Health science reports · 2025Review
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT.Dento maxillo facial radiology · 2024Article
- Classification of Ameloblastoma, Periapical Cyst, and Chronic Suppurative Osteomyelitis with Semi-Supervised Learning: The WaveletFusion-ViT Model Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Differential Diagnosis of OKC and SBC on Panoramic Radiographs: Leveraging Deep Learning Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Performance of a commercially available Generative Pre-trained Transformer (GPT) in describing radiolucent lesions in panoramic radiographs and establishing differential diagnoses.Clinical oral investigations · 2024Article
- Automatic segmentation of ameloblastoma on ct images using deep learning with limited data.BMC oral health · 2024Article
- Building Automation Pipeline for Diagnostic Classification of Sporadic Odontogenic Keratocysts and Non-Keratocysts Using Whole-Slide Images.Diagnostics (Basel, Switzerland) · 2023Article
- Novel Three-Dimensional and Non-Invasive Diagnostic Approach for Distinction between Odontogenic Keratocysts and Ameloblastomas.Dentistry journal · 2023Article
- Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases.Diagnostics (Basel, Switzerland) · 2022Review
- Magnetic resonance imaging texture analysis to differentiate ameloblastoma from odontogenic keratocyst.Scientific reports · 2022Article
- Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning technique.Dento maxillo facial radiology · 2022Article
- Improved Diagnostic Accuracy of Ameloblastoma and Odontogenic Keratocyst on Cone-Beam CT by Artificial Intelligence.Frontiers in oncology · 2021Article
- Review
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Authors and funding
7 authors.
Funding
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Abstract
objectiveTo analyse the automatic classification performance of a convolutional neural network (CNN), Google Inception v3, using tomographic images of odontogenic keratocysts (OKCs) and ameloblastomas (AMs).
methodsFor construction of the database, we selected axial multidetector CT images from patients with confirmed AM (
resultsThe accuracy and standard deviation (%) of cross-validation for the five iterations performed were 90.16 ± 0.95, 91.37 ± 0.57, 91.62 ± 0.19, 92.48 ± 0.16 and 91.21 ± 0.87, respectively. A higher error rate was observed for the classification of AM images.
conclusionThis study demonstrated a high classification accuracy of Google Inception v3 for tomographic images of OKCs and AMs. However, AMs images presented the higher error rate.
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