ArticleInternational journal of computer assisted radiology and surgery2021
Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs.
Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis.Oral radiology · 2024Pooled it
- Development of a Preliminary Diagnostic Tool for the Segmentation of Benign Jaw Lesions in CBCT Images Using nnU-Net v2: An Artificial Intelligence-Based Approach.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in the Diagnosis of Odontogenous Cysts and Ameloblastomas-A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- A deep learning approach for the diagnosis and recurrence prediction of OKC.Scientific reports · 2026Article
- 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
- Evaluation of the diagnostic reliability of an AI prototype in detecting clinical features from dental photographs-original research.Frontiers in dental medicine · 2026Article
- Deep learning for imaging diagnosis of jaw cystic lesions and maxillofacial tumors: A narrative review.The Journal of international medical research · 2025Review
- A deep learning approach based on YOLO v11 for automatic detection of jaw cysts.BMC oral health · 2025Article
- Performance of AI Chatbots in Preliminary Diagnosis of Maxillofacial Pathologies.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Automatic detection of orthodontically induced external root resorption based on deep convolutional neural networks using CBCT images.Scientific reports · 2025Article
- Artificial Intelligence Models Accuracy for Odontogenic Keratocyst Detection From Panoramic View Radiographs: A Systematic Review and Meta-Analysis.Health science reports · 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
- Concordance of clinician, Chat-GPT4, and ORAD diagnoses against histopathology in Odontogenic Keratocysts and tumours: a 15-Year New Zealand retrospective study.Oral and maxillofacial surgery · 2024Article
- Radiomic Parameters in Periapical Lesions: A CBCT Analysis Evaluating Volumetric Size, Cortical Expansion, Erosion, and Shape.European endodontic journal · 2024Article
- Tooth numbering with polygonal segmentation on periapical radiographs: an artificial intelligence study.Clinical oral investigations · 2024Article
- The Effect of Conservative vs. Radical Treatment of Ameloblastoma on Recurrence Rate and Quality of Life: An Umbrella Review.Journal of clinical medicine · 2024Review
- Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress.Dento maxillo facial radiology · 2024Review
- Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT.Dento maxillo facial radiology · 2024Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe differentiation of the ameloblastoma and odontogenic keratocyst directly affects the formulation of surgical plans, while the results of differential diagnosis by imaging alone are not satisfactory. This paper aimed to propose an algorithm based on convolutional neural networks (CNN) structure to significantly improve the classification accuracy of these two tumors.
methodsA total of 420 digital panoramic radiographs provided by 401 patients were acquired from the Shanghai Ninth People's Hospital. Each of them was cropped to a patch as a region of interest by radiologists. Furthermore, inverse logarithm transformation and histogram equalization were employed to increase the contrast of the region of interest (ROI). To alleviate overfitting, random rotation and flip transform as data augmentation algorithms were adopted to the training dataset. We provided a CNN structure based on a transfer learning algorithm, which consists of two branches in parallel. The output of the network is a two-dimensional vector representing the predicted scores of ameloblastoma and odontogenic keratocyst, respectively.
resultsThe proposed network achieved an accuracy of 90.36% (AUC = 0.946), while sensitivity and specificity were 92.88% and 87.80%, respectively. Two other networks named VGG-19 and ResNet-50 and a network trained from scratch were also used in the experiment, which achieved accuracy of 80.72%, 78.31%, and 69.88%, respectively.
conclusionsWe proposed an algorithm that significantly improves the differential diagnosis accuracy of ameloblastoma and odontogenic keratocyst and has the utility to provide a reliable recommendation to the oral maxillofacial specialists before surgery.
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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.