Abstract

Ischemic stroke remains a leading cause of mortality and long-term disability worldwide, with risk prediction 
constrained by incomplete characterization of biological heterogeneity and limited integration of high-dimensional 
data. Advances in long-read genomic sequencing and neuroimaging now enable comprehensive profiling of 
structural variants, haplotypes, epigenetic signals, and imaging biomarkers directly linked to cerebrovascular 
pathology. This paper examines the integration of long-read sequencing data with imaging biomarkers for stroke 
risk prediction, with particular emphasis on interpretability, bias, real-world performance, and equity-oriented 
implementation. We review methodological foundations for multimodal data fusion, feature extraction from long
read sequencing, and quantitative imaging biomarker analysis, highlighting the complementary biological insights 
each modality provides. We further analyze modeling strategies that balance predictive performance with clinical 
interpretability, including fairness-aware machine-learning approaches to address demographic and technical 
biases. Real-world validation challenges, external generalizability, calibration, and clinical utility are critically 
assessed, alongside governance, privacy, and regulatory considerations. By situating genomic imaging integration 
within healthcare system constraints and equity frameworks, this work outlines a translational pathway for 
responsible deployment. We conclude that integrative genomic imaging models hold substantial promise for 
improving stroke risk stratification, provided that interpretability, bias mitigation, and equitable access are 
embedded throughout model development and implementation.