An AI Algorithm for Lymphocyte Focus Score of Minor Salivary Gland Biopsy Samples for Diagnosing Sjogren's Syndrome
NCT ID: NCT06437652
Last Updated: 2024-07-05
Study Results
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Basic Information
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RECRUITING
1000 participants
OBSERVATIONAL
2023-10-01
2024-09-30
Brief Summary
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The research will：
1. Develop and debug the AI algorithm for lymphocyte focus score in whole slide images of labial minor SG biopsy samples for diagnosing Sjogren's Syndrome;
2. Internal test of the AI algorithm;
3. Clinical validation of the AI algorithm with blind method in multiple centers; 4）Built a remote AI-assisted pathological interpretation platform for lymphocyte focus score in labial SG for the global and Explore its clinical application.
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Detailed Description
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2. Internal test of the AI algorithm; A total of 500 additional digital pathological images of labial gland biopsy tissues are collected from Sun Yat-sen Memorial Hospital of Sun Yat-sen University. The ground truth of gland tissue area and lymphocyte foci numbers in each images is interpreted by three senior pathologists with over 5 years of related experience. The AI algorithm's accuracy, specificity, sensitivity, positive predictive value and negative predictive value in evaluating the area of labial gland and the number of lymphocyte foci are calculated. Comparison of whether the image meets the criteria for Sjögren's syndrome (focus score greater than 1) is also conducted between the AI algorithm and the ground truth.
3. Clinical validation of the AI algorithm with blind method in multiple centers; A total of 600 additional digital pathological images of labial gland biopsy tissues are collected from six external centers. The ground truth of gland tissue area and lymphocyte foci numbers in each images is interpreted by three senior pathologists with over 5 years of related experience. The AI algorithm's accuracy, specificity, sensitivity, positive predictive value and negative predictive value in evaluating the area of labial gland and the number of lymphocyte foci are calculated. Comparison of whether the image meets the criteria for Sjögren's syndrome (focus score greater than 1) is also conducted between the AI algorithm and the ground truth.
4）Built a remote AI-assisted pathological interpretation platform for lymphocyte focus score in labial SG for the global and Explore its clinical application.
Digital pathological images of labial gland biopsy tissue can be uploaded to the Labial Gland Pathological Focus Score Remoting platform. AI-assisted pathological interpretation on gland tissue area, lymphocyte foci numbers, and whether meeting the criteria for Sjögren's syndrome (focus score greater than 1) is compared with the ground truth.
Conditions
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Study Design
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COHORT
CROSS_SECTIONAL
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
2. Excessive tissue defects caused by incomplete sectioning or poor staining on slides;
3. Absence of labial gland;
4. Insufficient clarity in the image.
18 Years
70 Years
ALL
No
Sponsors
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Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
OTHER
Responsible Party
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Moyingqian
Director
Locations
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Ying-Qian Mo
Guangzhou, Guangdong, China
Countries
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Central Contacts
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Other Identifiers
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SYSKY-2023-915-01
Identifier Type: -
Identifier Source: org_study_id
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