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Schaab, S.* ; Bauer, C.J.* ; Schäfer, V.S.* ; Albarqouni, S.

Artery-fusion deep learning for enhanced ultrasound diagnosis of giant cell arteritis.

In: (Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024)). Springer, 2025. 396-406 (LNEE ; 1372 LNEE)

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Giant Cell Arteritis (GCA) is a serious autoimmune disease that affects large and medium-size arteries, potentially leading to severe complications like vision loss if not diagnosed promptly. Current diagnostic methods rely heavily on clinical judgment and ultrasound imaging techniques, which require experience and expertise. This work investigates the use of supervised deep learning to improve the detection of hypoechoic wall thickening in ultrasound images, a key indicator of GCA. We developed an affordable and efficient artery-fusion deep learning model that receives an ultrasound image as input and considers artery type to enhance the detection accuracy. Our Artery Fusion Model, which integrates simple yet crucial artery-type information, demonstrated superior diagnostic accuracy with an F1-score of 81% and 74% and an AUROC score of 0.94 and 0.87 for larger arteries (AAX and ATC), outperforming both the Artery-Specific and Combined models by 2.7% (AAX) or 8.3% (ATC) and 2.8% (AAX) or 0.9% (ATC), respectively. However, the performance was lower for smaller arteries (ATF and ATP), reflecting the inherent challenges associated with these vessels. We employed Monte Carlo Batch Normalization and Class Activation Maps to improve interpretability and reliability. Our results demonstrate that uncertainty quantification enhances model performance by excluding uncertain predictions, underscoring its potential to revolutionize GCA detection and diagnosis.

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