Analysis of Waveform Parameters in the Retinal Vasculature via Mathematical Modeling and Data Analytics Methods

Area of Science:

• Ophthalmology
• Biomedical Engineering
• Medical Imaging

Background:

• Retinal hemodynamics analysis is crucial for understanding ocular health.
• Previous models lacked patient-specific personalization and integration of diverse clinical data.
• Developing accurate models requires combining mathematical approaches with data analytics.

Purpose of the Study:

• To develop and evaluate a patient-specific mathematical modeling approach for retinal hemodynamics.
• To integrate clinical measurements and physiological insights into an in silico framework.
• To assess the impact of methodological choices on clinically relevant blood flow biomarkers.

Main Methods:

• Utilized patient-specific input data including central retinal artery (CRA) velocity profile, systemic blood pressure, heart rate, and intraocular pressure.
• Combined automatic image processing techniques with mathematical modeling for data integration.
• Employed extensive validation and comparison with prior studies and introduced a novel Wasserstein distance metric for temporal analysis.

Main Results:

• Demonstrated the effectiveness of patient-specific input data in improving the accuracy of retinal blood flow modeling.
• Highlighted the significance of methodological considerations in data processing and model parameterization.
• Showcased the utility of the Wasserstein distance metric for monitoring dynamic changes in retinal vascular function.

Conclusions:

• Patient-specific mathematical modeling, coupled with automatic image processing, provides robust and clinically relevant insights into retinal vasculature.
• Personalized input data is essential for accurate hemodynamic analysis and biomarker identification.
• The developed framework offers a promising tool for advancing the understanding and monitoring of retinal vascular diseases.