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Abstract #4274

Deep Learning-Based Quantification of Intraplaque Hemorrhage Reveals Impacts on Long-Term Carotid Plaque Progression

Yin Guo1, Daniel S Hippe2, Xin Wang3, Gador Canton4, Kaiyu Zhang1, Angie Tang4, Marina M Ferguson4, Mahmud Mossa-Basha4, Niranjan Balu4, Thomas S Hatsukami4, and Chun Yuan4,5
1Bioengineering, University of Washington, Seattle, WA, United States, 2Clinical Biostatistics, Fred Hutchison Cancer Center, Seattle, WA, United States, 3Electrical and Computer Engineering, University of Washington, Seattle, WA, United States, 4Radiology, University of Washington, Seattle, WA, United States, 5Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States

Synopsis

Keywords: Analysis/Processing, Analysis/Processing

Motivation: Carotid atherosclerosis is a leading cause of stroke, and intraplaque hemorrhage (IPH) is a significant predictor of plaque progression and rupture. Understanding the role of IPH in asymptomatic patients over time is essential for effective management.

Goal(s): Develop a precise, reproducible deep learning-based algorithm for IPH segmentation and apply it to analyze long-term plaque burden evolution.

Approach: A segmentation algorithm was trained using 3D-SNAP images. The algorithm's validation included histology comparisons and reproducibility analysis. It was applied to long-term repeated scans to assess the relationship between IPH and plaque burden.

Results: IPH presence and volume were significantly associated with greater plaque burden progression.

Impact: This study underscores the significance of IPH in carotid plaque progression, offering a precise deep learning-based tool for monitoring. It enables more effective risk assessment and personalized management strategies, sparking new research into long-term IPH effects on asymptomatic patients.

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Keywords