iEVIP: a dual-inspired intelligent platform for smart response extracellular vesicle isolation and single-vesicle profiling

Extracellular vesicles (EVs) are promising non-invasive biomarkers for early cancer detection, yet clinical translation is limited by membrane fouling during isolation and vesicle aggregation during single-vesicle imaging. Here, iEVIP (intelligent extracellular vesicle isolation and profiling), an integrated dual-inspired platform, combines intelligent pulsatile filtration, blood-smear-inspired nanoscale organization and machine-learning-based classification. Real-time transmembrane-pressure monitoring triggers back-aspiration pulses upon membrane fouling, promoting membrane regeneration and stable EV recovery from plasma. A wettability-assisted nano-smear array reduces aggregation and fluorescence overlap, enabling high-throughput multiplexed single-EV imaging. iEVIP achieves 95.32% classification accuracy for cell-line-derived EVs and up to 80.37% in exploratory clinical cohorts using random forest algorithm. By bridging macroscopic engineering principles with nanoscale bioanalysis, iEVIP provides a scalable framework for high-throughput single-EV analysis and exploratory clinical sample classification. However, the limited cohort size and absence of cross-center external validation constrain generalizability, and larger independent cohorts are needed to establish diagnostic robustness and clinical applicability.