ArticleDento maxillo facial radiology2020
Usefulness of a deep learning system for diagnosing Sjögren's syndrome using ultrasonography images.
Article in Dento maxillo facial radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Does ultrasound elastography have a role as a diagnostic method for Sjögren's syndrome in the salivary glands? A systematic review.Oral radiology · 2024Pooled it
- Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review.Pain research & management · 2021Pooled it
- Detecting Sjögren's Disease from Parotid Gland Ultrasound Radiomics.Rheumatology (Oxford, England) · 2026Article
- Diagnostic performance of conventional ultrasound and shear wave elastography of the parotid glands in primary Sjögren's syndrome.BMC medical imaging · 2026Article
- From subjective assessment to data-driven diagnosis: the AI revolution in multimodal early detection of Sjögren's disease.Frontiers in immunology · 2026Review
- 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
- 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
- AI for imaging evaluation in rheumatology: applications of radiomics and computer vision-current status, future prospects and potential challenges.Rheumatology advances in practice · 2025Review
- Anti-centromere antibody positivity is an independent variable associated with salivary gland ultrasonography score in Sjögren's syndrome.Scientific reports · 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
- Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma.Cancers · 2024Article
- A brief exploration of artificial intelligence in dental healthcare: a narrative review.F1000Research · 2024Review
- Machine and Deep Learning Approaches Applied to Classify Gougerot-Sjögren Syndrome and Jointly Segment Salivary Glands.Bioengineering (Basel, Switzerland) · 2023Article
- Overview of Ultrasound in Dentistry for Advancing Research Methodology and Patient Care Quality with Emphasis on Periodontal/Peri-implant Applications.Zeitschrift fur medizinische Physik · 2023Review
- Effect of deep transfer learning with a different kind of lesion on classification performance of pre-trained model: Verification with radiolucent lesions on panoramic radiographs.Imaging science in dentistry · 2023Article
- Machine Learning in Dentistry: A Scoping Review.Journal of clinical medicine · 2023Article
- Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images.BMC oral health · 2022Article
- Transfer learning in a deep convolutional neural network for implant fixture classification: A pilot study.Imaging science in dentistry · 2022Article
- The Diagnostic Value of Ultrasound-Based Deep Learning in Differentiating Parotid Gland Tumors.Journal of oncology · 2022Article
- Automatic segmentation of the temporomandibular joint disc on magnetic resonance images using a deep learning technique.Dento maxillo facial radiology · 2022Article
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12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesWe evaluated the diagnostic performance of a deep learning system for the detection of Sjögren's syndrome (SjS) in ultrasonography (US) images, and compared it with the performance of inexperienced radiologists.
methods100 patients with a confirmed diagnosis of SjS according to both the Japanese criteria and American-European Consensus Group criteria and 100 non-SjS patients that had a dry mouth and suspected SjS but were definitively diagnosed as non-SjS were enrolled in this study. All the patients underwent US scans of both the parotid glands (PG) and submandibular glands (SMG). The training group consisted of 80 SjS patients and 80 non-SjS patients, whereas the test group consisted of 20 SjS patients and 20 non-SjS patients for deep learning analysis. The performance of the deep learning system for diagnosing SjS from the US images was compared with the diagnoses made by three inexperienced radiologists.
resultsThe accuracy, sensitivity and specificity of the deep learning system for the PG were 89.5, 90.0 and 89.0%, respectively, and those for the inexperienced radiologists were 76.7, 67.0 and 86.3%, respectively. The deep learning system results for the SMG were 84.0, 81.0 and 87.0%, respectively, and those for the inexperienced radiologists were 72.0, 78.0 and 66.0%, respectively. The AUC for the inexperienced radiologists was significantly different from that of the deep learning system.
conclusionsThe deep learning system had a high diagnostic ability for SjS. This suggests that deep learning could be used for diagnostic support when interpreting US images.
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