ArticleMedical principles and practice : international journal of the Kuwait University, Health Science Centre2022
A Deep Learning Model for Idiopathic Osteosclerosis Detection on Panoramic Radiographs.
Article in Medical principles and practice : international journal of the Kuwait University, Health Science Centre, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Detection of Jaw Lesions on Panoramic Radiographs Using Deep Learning Method.Journal of imaging informatics in medicine · 2026Article
- Development and validation of an artificial intelligence model based on liver CSE-MRI fat maps for predicting dyslipidemia.PLOS digital health · 2026Article
- Evaluation of the effectiveness of artificial intelligence models in radiopaque and radiolucent lesions of the maxillofacial region on panoramic radiographs.Oral radiology · 2026Article
- Artificial intelligence-assisted identification of condensing osteitis and idiopathic osteosclerosis on panoramic radiographs.Scientific reports · 2025Article
- A deep learning AI model for determining the relationship between X-Ray detectors and patient positioning in chest radiography.PloS one · 2025Article
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Performance evaluation of a deep learning model for automatic detection and localization of idiopathic osteosclerosis on dental panoramic radiographs.Scientific reports · 2024Article
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9 authors.
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Abstract
objectiveThe purpose of the study was to create an artificial intelligence (AI) system for detecting idiopathic osteosclerosis (IO) on panoramic radiographs for automatic, routine, and simple evaluations. SUBJECT AND
methodsIn this study, a deep learning method was carried out with panoramic radiographs obtained from healthy patients. A total of 493 anonymized panoramic radiographs were used to develop the AI system (CranioCatch, Eskisehir, Turkey) for the detection of IOs. The panoramic radiographs were acquired from the radiology archives of the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University. GoogLeNet Inception v2 model implemented with TensorFlow library was used for the detection of IOs. Confusion matrix was used to predict model achievements.
resultsFifty IOs were detected accurately by the AI model from the 52 test images which had 57 IOs. The sensitivity, precision, and F-measure values were 0.88, 0.83, and 0.86, respectively.
conclusionDeep learning-based AI algorithm has the potential to detect IOs accurately on panoramic radiographs. AI systems may reduce the workload of dentists in terms of diagnostic efforts.
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