ArticleFrontiers in oncology2021
Improved Diagnostic Accuracy of Ameloblastoma and Odontogenic Keratocyst on Cone-Beam CT by Artificial Intelligence.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 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
- Exploring the landscape of artificial intelligence in dental and maxillofacial radiology: a bibliometric analysis of studies and trends.Annals of medicine and surgery (2012) · 2026Review
- Enhancing the preoperative differentiation of ameloblastoma and odontogenic keratocyst using pathomics-guided radiomics: a pilot study.BMC oral health · 2026Article
- Evaluation of Cone-Beam Computed Tomography Images with Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset for Benchmarking Deep Learning Algorithms.Scientific data · 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
- The transformative impact of artificial intelligence on periodontal care: A review.Bioinformation · 2026Article
- Review
- Toward Precision Diagnosis of Maxillofacial Pathologies by Artificial Intelligence Algorithms: A Systematic Review.Journal of maxillofacial and oral surgery · 2025Review
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 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
- The Use of Artificial Intelligence in Endodontics.Journal of dental research · 2024Review
- Odontogenic Keratocyst in Maxillary Sinus with Ectopic Third Molar: A Case Report.The American journal of case reports · 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
- Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress.Dento maxillo facial radiology · 2024Review
- Classification of Ameloblastoma, Periapical Cyst, and Chronic Suppurative Osteomyelitis with Semi-Supervised Learning: The WaveletFusion-ViT Model Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Odontogenic Tumors: A Challenge for Clinical Diagnosis and an Opportunity for AI Innovation.Journal of dentistry (Shiraz, Iran) · 2024Article
- Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts.International journal of oral science · 2024Article
- Automatic segmentation of ameloblastoma on ct images using deep learning with limited data.BMC oral health · 2024Article
- Odontogenic cystic lesion segmentation on cone-beam CT using an auto-adapting multi-scaled UNet.Frontiers in oncology · 2024Article
Corrections and comments
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7 authors.
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Abstract
objectiveThe purpose of this study was to utilize a convolutional neural network (CNN) to make preoperative differential diagnoses between ameloblastoma (AME) and odontogenic keratocyst (OKC) on cone-beam CT (CBCT).
methodsThe CBCT images of 178 AMEs and 172 OKCs were retrospectively retrieved from the Hospital of Stomatology, Wuhan University. The datasets were randomly split into a training dataset of 272 cases and a testing dataset of 78 cases. Slices comprising lesions were retained and then cropped to suitable patches for training. The Inception v3 deep learning algorithm was utilized, and its diagnostic performance was compared with that of oral and maxillofacial surgeons.
resultsThe sensitivity, specificity, accuracy, and F1 score were 87.2%, 82.1%, 84.6%, and 85.0%, respectively. Furthermore, the average scores of the same indexes for 7 senior oral and maxillofacial surgeons were 60.0%, 71.4%, 65.7%, and 63.6%, respectively, and those of 30 junior oral and maxillofacial surgeons were 63.9%, 53.2%, 58.5%, and 60.7%, respectively.
conclusionThe deep learning model was able to differentiate these two lesions with better diagnostic accuracy than clinical surgeons. The results indicate that the CNN may provide assistance for clinical diagnosis, especially for inexperienced surgeons.
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