A graph-learning based model for automatic diagnosis of Sjogren's syndrome on digital pathological images: a multicentre cohort study.

ABSTRACT:

Background

Sjögren's Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations.

Methods

The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature.

SUBMITTER: Wu R 

PROVIDER: S-EPMC11308146 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature