Preliminary study on the application of deep learning system to diagnosis of Sjögren's syndrome on CT images

Objectives: This 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. Methods: CT 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). Results: The 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. Conclusions: The 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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Usefulness of a deep learning system for diagnosing Sjögren’s syndrome using ultrasonography images

Dentomaxillofacial Radiology ◽

10.1259/dmfr.20190348 ◽

2020 ◽

Vol 49 (3) ◽

pp. 20190348 ◽

Cited By ~ 2

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.

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