Whole-Volume ADC Histogram Analysis of Parotid Glands to Identify Patients With Sjögren's Syndrome

2018 ◽  
Author(s):  
Chen Chu ◽  
Huayong Zhang ◽  
Qiangian Feng ◽  
Yun Zhu ◽  
Weibo Chen ◽  
...  
Keyword(s):  
Sjögren’S Syndrome ◽  
Sjögren's Syndrome ◽  
Sjogren’S Syndrome ◽  
Sjogren's Syndrome ◽  
Histogram Analysis ◽  
Parotid Glands ◽  
Sjögren‘S Syndrome

Use of T1ρMR imaging in Sjögren's syndrome with normal appearing parotid glands: Initial findings

2016 ◽  
Vol 45 (4) ◽  
pp. 1005-1012 ◽  
Author(s):  
Chen Chu ◽  
Nan Zhou ◽  
Huayong Zhang ◽  
Xin Dou ◽  
Ming Li ◽  
...  
Keyword(s):  
Sjögren’S Syndrome ◽  
Sjögren's Syndrome ◽  
Sjogren’S Syndrome ◽  
Sjogren's Syndrome ◽  
Parotid Glands ◽  
Sjögren‘S Syndrome

Subclinical Sjögren's syndrome: A significant67gallium accumulation in the orbits and parotid glands

1998 ◽  
Vol 40 (6) ◽  
pp. 621-623 ◽  
Author(s):  
Hiroshi Tanaka ◽  
Norio Onodera ◽  
Ryosuke Ito ◽  
Akifumi Higuchi ◽  
Yasushi Suzuki ◽  
...  
Keyword(s):  
Sjögren’S Syndrome ◽  
Sjögren's Syndrome ◽  
Sjogren’S Syndrome ◽  
Sjogren's Syndrome ◽  
Parotid Glands ◽  
Sjögren‘S Syndrome

scholarly journals Usefulness of a deep learning system for diagnosing Sjögren’s syndrome using ultrasonography images

2020 ◽  
Vol 49 (3) ◽  
pp. 20190348 ◽  
Author(s):  
Yoshitaka Kise ◽  
Mayumi Shimizu ◽  
Haruka Ikeda ◽  
Takeshi Fujii ◽  
Chiaki Kuwada ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sjögren’S Syndrome ◽  
Sjögren's Syndrome ◽  
Test Group ◽  
Training Group ◽  
Sjogren’S Syndrome ◽  
Sjogren's Syndrome ◽  
Learning System ◽  
Parotid Glands ◽  
Sjögren‘S Syndrome

Objectives: We 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. Methods: 100 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. Results: The 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. Conclusions: The 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.

scholarly journals Systems biology analysis of sjögren's syndrome and mucosa-associated lymphoid tissue lymphoma in parotid glands

2009 ◽  
Vol 60 (1) ◽  
pp. 81-92 ◽  
Author(s):  
Shen Hu ◽  
Michael Zhou ◽  
Jiang Jiang ◽  
Jianghua Wang ◽  
David Elashoff ◽  
...  
Keyword(s):  
Systems Biology ◽  
Sjögren’S Syndrome ◽  
Lymphoid Tissue ◽  
Sjögren's Syndrome ◽  
Sjogren’S Syndrome ◽  
Sjogren's Syndrome ◽  
Parotid Glands ◽  
Sjögren‘S Syndrome
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