Artificial Intelligence Based Models for Primary Sjögren's Syndrome Diagnosis
NCT ID: NCT06982482
Last Updated: 2025-05-21
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
27432 participants
OBSERVATIONAL
2013-01-01
2025-01-01
Brief Summary
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Can AI-based algorithms accurately diagnose Primary Sjögren's Syndrome by analyzing laboratory test results, and do they outperform traditional diagnostic criteria in Chinese populations?
Researchers will retrospectively analyze anonymized clinical records and laboratory data (e.g., autoantibody levels, inflammatory markers) from patients with suspected or confirmed PSS across multiple medical centers in China. No new interventions will be administered, as the study utilizes existing historical data to train and validate the AI models. The performance of AI algorithms will be compared with current diagnostic standards (e.g., ACR/EULAR criteria) in terms of sensitivity, specificity, and clinical utility.
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Detailed Description
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Conditions
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Study Design
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CASE_CONTROL
RETROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
* Control groups: Individuals with non-pSS autoimmune diseases (e.g., rheumatoid arthritis, systemic lupus erythematosus) or non-autoimmune conditions (e.g., dry eye/sicca symptoms without systemic autoimmunity).
Exclusion Criteria
* Not newly diagnosed in any of the hospitals.
* Without any available laboratory tests.
18 Years
65 Years
ALL
Yes
Sponsors
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The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
OTHER
Responsible Party
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Principal Investigators
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Xinran Yuan, MD
Role: STUDY_CHAIR
Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University
Locations
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Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University
Nanjing, Jiangsu, China
Countries
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Other Identifiers
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B2022-529-04
Identifier Type: -
Identifier Source: org_study_id
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