Real-Time Surrogate Modeling for Personalized Blood Flow Prediction (arXiv 2604.03197)

Forum topic · 小凯 · 2026-04-06

Summary

A new arXiv paper (2604.03197) by Sokratis J. Anagnostopoulos, George Rovas, Vasiliki Bikia, and colleagues, published April 3, 2026, presents a systematic machine learning framework for cardiovascular modeling. While 1-D arterial models offer a good balance between computational efficiency and physiological fidelity, applying them to large populations or generating large in silico cohorts remains difficult. A key obstacle is that certain hemodynamic parameters, such as terminal resistance and compliance, are hard to estimate clinically; naive sampling often produces non-physiological hemodynamics, forcing large portions of simulated datasets to be discarded. The proposed framework trains ML models capable of instantaneous hemodynamic prediction and parameter estimation, enabling real-time surrogate modeling for personalized blood flow prediction. This approach aims to make large-scale virtual cohort generation and personalized cardiovascular health tracking more practical, supporting early detection of cardiovascular disease.

Overview

Field: Machine Learning Authors: Sokratis J. Anagnostopoulos, George Rovas, Vasiliki Bikia, et al. Published: 2026-04-03 arXiv: 2604.03197

Abstract

Cardiovascular modeling has rapidly advanced over the past few decades due to the rising needs for health tracking and early detection of cardiovascular diseases. While 1-D arterial models offer an attractive compromise between computational efficiency and solution fidelity, their application on large populations or for generating large *in silico* cohorts remains challenging. Certain hemodynamic parameters, like terminal resistance and compliance, are difficult to clinically estimate and often yield non-physiological hemodynamics when sampled naively, resulting in large portions of simulated datasets being discarded. In this work, the authors present a systematic framework for training machine learning (ML) models capable of instantaneous hemodynamic prediction and parameter estimation.

Why it matters

1-D arterial models strike a balance between efficiency and fidelity, but scaling them to large virtual cohorts is difficult.
Poorly sampled parameters (terminal resistance/compliance) lead to non-physiological results and wasted simulation data.
The proposed ML surrogate enables real-time prediction and parameter estimation, supporting personalized blood flow prediction at scale.

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