An AI Algorithm for Lymphocyte Focus Score of Minor Salivary Gland Biopsy Samples for Diagnosing Sjogren's Syndrome

Summary

The aim of this research is to discover an artificial intelligence (AI) algorithm for lymphocyte focus score in whole slide images of labial minor salivary gland (SG) biopsy samples for diagnosing Sjogren's Syndrome, in order to enhance the precision of pathological interpretation of labial minor SG biopsy samples in patients with suspected Sjogren's syndrome and aid clinicians make an accurate diagnose. A remote AI-assisted pathological interpretation platform for lymphocyte focus score in labial SG will be built for the global based on the research results. The research will propose the AI-assisted pathological interpretation of lymphocyte focus score in labial minor SG biopsy samples in the future guidelines for the diagnosis and treatment of Sjogren's syndrome. The research will： 1. Develop and debug the AI algorithm for lymphocyte focus score in whole slide images of labial minor SG biopsy samples for diagnosing Sjogren's Syndrome; 2. Internal test of the AI algorithm; 3. Clinical validation of the AI algorithm with blind method in multiple centers; 4）Built a remote AI-assisted pathological interpretation platform for lymphocyte focus score in labial SG for the global and Explore its clinical application.

Eligibility

Inclusion Criteria:

* The original format of digital pathological images of labial gland biopsy tissue from patients with suspected Sjögren's Syndrome uploaded to the designated platform.

Exclusion Criteria:

1. Overlapping layers of cells due to excessively thick sections;
2. Excessive tissue defects caused by incomplete sectioning or poor staining on slides;
3. Absence of labial gland;
4. Insufficient clarity in the image.

Conditions2

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