Semantic-Aware Approximate Nearest Neighbor Search for Personalized Cardiovascular Monitoring Using PPG Foundation Models - Abhishek M. Srinivasan; Ganjemin Rrawford; Vikram R. Batra (Bioinformatics Insights and Analytics, 2026)
Photoplethysmography has emerged as a ubiquitous modality for ambulatory cardiovascular assessment, but its full clinical potential remains constrained by the heterogeneity of signal morphology across diverse populations, sensor configurations, and pathophysiological manifestations. Foundation models pre-trained on large-scale photoplethysmographic repositories offer a unified representational space that can capture subtle physiological signatures, yet the challenge of efficiently retrieving clinically relevant exemplars from massive embedding databases for personalized inference has not been addressed. This paper introduces a semantic-aware approximate nearest neighbor search framework tailored to cardiovascular monitoring tasks that leverages photoplethysmography foundation model embeddings. The architectural design couples deep semantic hashing with graph-based indexing to enable millisecond-latency retrieval of diagnostically similar physiological states while preserving clinically meaningful semantics. We systematically analyze the trade-offs between retrieval accuracy, computational efficiency, and interpretability through the lens of multi-level infrastructure spanning edge wearable devices, fog gateways, and cloud-based model repositories. Critical considerations surrounding robustness to distributional shift, demographic fairness in semantic similarity spaces, differential privacy during query execution, and sustainable model lifecycle management are examined. The paper further explores the governance structures required to maintain trustworthiness when personalized retrieval systems operate across health system boundaries, and proposes a policy-oriented architecture that embeds auditability and federated accountability mechanisms directly into the retrieval pipeline. Our analysis suggests that semantic-aware approximate nearest neighbor search, when integrated with structured fine-grained access controls and continuous monitoring for concept drift, can serve as a pivotal enabling technology for the next generation of equitable and efficient cardiovascular digital twins.