ArticleDento maxillo facial radiology2019
Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images.
Article in Dento maxillo facial radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review.Pain research & management · 2021Pooled it
- Diagnostic performance of conventional ultrasound and shear wave elastography of the parotid glands in primary Sjögren's syndrome.BMC medical imaging · 2026Article
- Accuracy of AI Tools in the Diagnosis of Benign, Potentially Malignant and Malignant Oral Lesions: A Pilot Study.Journal of clinical medicine · 2026Article
- Comparative diagnostic accuracy of ChatGPT models in salivary gland disease: a multimodal vignette-based evaluation.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Attitudes and perceptions of dental students and interns toward AI in dentistry: a cross-sectional survey in a Saudi population.BMC medical education · 2026Article
- From subjective assessment to data-driven diagnosis: the AI revolution in multimodal early detection of Sjögren's disease.Frontiers in immunology · 2026Review
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- A Review of the Current Clinical Aspects of Sjögren's Disease: Geographical Difference, Classification/Diagnostic Criteria, Recent Advancements in Diagnostic Methods, and Molecular Targeted Therapy.Journal of clinical medicine · 2025Review
- Toward Precision Diagnosis of Maxillofacial Pathologies by Artificial Intelligence Algorithms: A Systematic Review.Journal of maxillofacial and oral surgery · 2025Review
- Preliminary approach to creation of a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques on computed tomography images of the parotid glands.Imaging science in dentistry · 2025Article
- Applications of Artificial Intelligence in Dental Medicine: A Critical Review.International dental journal · 2025Review
- Automated Age Estimation from OPG Images and Patient Records Using Deep Feature Extraction and Modified Genetic-Random Forest.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- Automated segmentation and deep learning classification of ductopenic parotid salivary glands in sialo cone-beam CT images.International journal of computer assisted radiology and surgery · 2025Article
- The relationship between radiomic features in CT images and the salivary gland SPECT/CT standardised uptake value using 3D analysis.Polish journal of radiology · 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
- The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study.BMC oral health · 2024Article
- Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs.Imaging science in dentistry · 2024Article
- Review
- Review
Corrections and comments
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study estimated the diagnostic performance of a deep learning system for detection of Sjögren's syndrome (SjS) on CT, and compared it with the performance of radiologists.
methodsCT images were assessed from 25 patients confirmed to have SjS based on the both Japanese criteria and American-European Consensus Group criteria and 25 control subjects with no parotid gland abnormalities who were examined for other diseases. 10 CT slices were obtained for each patient. From among the total of 500 CT images, 400 images (200 from 20 SjS patients and 200 from 20 control subjects) were employed as the training data set and 100 images (50 from 5 SjS patients and 50 from 5 control subjects) were used as the test data set. The performance of a deep learning system for diagnosing SjS from the CT images was compared with the diagnoses made by six radiologists (three experienced and three inexperienced radiologists).
resultsThe accuracy, sensitivity, and specificity of the deep learning system were 96.0%, 100% and 92.0%, respectively. The corresponding values of experienced radiologists were 98.3%, 99.3% and 97.3% being equivalent to the deep learning, while those of inexperienced radiologists were 83.5%, 77.9% and 89.2%. The area under the curve of inexperienced radiologists were significantly different from those of the deep learning system and the experienced radiologists.
conclusionsThe deep learning system showed a high diagnostic performance for SjS, suggesting that it could possibly be used for diagnostic support when interpreting CT images.
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