GCA-US-AI: Deep learning–based ultrasound diagnosis of Giant Cell Arteritis

Abstract

In close interdisciplinary cooperation with clinical rheumatology, this project addresses the highly operator-dependent and time-critical ultrasound diagnosis of Giant Cell Arteritis (GCA), a vasculitis associated with irreversible vision loss. Together with our partners, we curated and annotated an in-house dataset of 3,800 ultrasound examinations from 344 patients, constituting one of the largest clinically validated datasets for ultrasound-based GCA diagnosis. Methodologically, we developed artery-aware deep learning models that integrate anatomical context into ultrasound image analysis, achieving robust performance improvements over artery-agnostic baselines (AUROC up to ~0.9 for larger arteries). Uncertainty estimation and visual explainability were incorporated to support reliable clinical interpretation.

Perspective

The collaboration is ongoing and continues to serve as a basis for joint publications, supervised theses, and continued dataset extension (in-house already ~15,000 images) toward a multi-center cohort. Current work explores vision–language models integrating imaging and clinical data and forms the basis of a planned joint DFG proposal coordinated together with the clinical partners.