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Artificial Intelligence Based Models for Primary Sjögren's Syndrome Diagnosis

NCT06982482 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 27432

Last updated 2025-05-21

No results posted yet for this study

Summary

The goal of this observational study is to develop and validate artificial intelligence (AI)-driven models for improving the diagnosis of Primary Sjögren's Syndrome (PSS) using routine laboratory test data. The main question it aims to answer is:

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.

Conditions

  • Primary Sjögren's Syndrome (pSS)

Sponsors & Collaborators

  • The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

    lead OTHER

Principal Investigators

  • Xinran Yuan, MD · Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2013-01-01
Primary Completion
2023-01-01
Completion
2025-01-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06982482 on ClinicalTrials.gov