Artificial Intelligence Based Models for Primary Sjögren's Syndrome Diagnosis

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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06982482

Enrollment

27432

Registered

2025-05-21

Start date

2013-01-01

Completion date

2025-01-01

Last updated

2025-05-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Primary Sjögren's Syndrome (pSS)

Brief 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.

Interventions

None listed

Study design

Observational model

CASE_CONTROL

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 65 Years

Healthy volunteers

Yes

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.

Design outcomes

Primary

Measure	Time frame	Description
Diagnostic Accuracy of AI Models for Primary Sjögren's Syndrome (pSS)	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.

Countries

China

Outcome results