Completed

NCT Number: NCT06982482

Artificial Intelligence Based Models for Primary Sjögren's Syndrome Diagnosis

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.

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Key information

Conditions

Primary Sjögren's Syndrome (pSS)

Age range

18 year–65 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University

Nanjing, Jiangsu, 210008, China

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Patients with clinician-diagnosed primary Sjögren's syndrome (pSS) meeting the 2016 ACR/EULAR or 2002 ACEG classification criteria (objective oral/ocular dryness, positive anti-SSA/Ro antibodies, or focal lymphocytic sialadenitis on biopsy).
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

Pregnancy, breastfeeding, with a clear diagnosis of other autoimmune diseases, severe infection and malignant tumors.
Not newly diagnosed in any of the hospitals.
Without any available laboratory tests.

Treatment and study plan

Primary outcomes

Diagnostic Accuracy of AI Models for Primary Sjögren's Syndrome (pSS)

Time frame: Data Collection Period: January 1, 2013, to January 31, 2023 (retrospective analysis of historical records). Model Development and Validation: Completed within 12 months of data aggregation.

The primary outcome measure is the comparative diagnostic accuracy of the AI-driven model versus the 2016 ACR/EULAR classification criteria for PSS. Accuracy will be quantified using sensitivity (true positive rate), specificity (true negative rate), and area under the receiver operating characteristic curve (AUC-ROC). The AI model's performance will be validated against a gold-standard clinician diagnosis based on comprehensive clinical, serological, and histological assessments.

Sponsors and collaborators

Lead sponsor

The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

Other

Registry information

Official study title

Artificial Intelligence Based Models for Primary Sjögren's Syndrome Diagnosis Using Laboratory Data: A Chinese Multicenter Retrospective Study

Important dates

Study start: 2013
Primary completion: 2023
Study completion: 2025
First posted: May 21, 2025
Registry last updated: May 21, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.