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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