Artificial Intelligence Based Models for Primary Sjögren's Syndrome Diagnosis

The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School27,432 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

27,432

Conditions

Primary Sjögren's Syndrome (pSS)

Eligibility

Sex: ALLMin age: 18 YearsMax age: 65 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

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

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.

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.