Assessing Coronary Microvascular Dysfunction using Angiography-based Data-driven Methods

2025-12-23

AI Summary

Coronary microvascular dysfunction (CMD) is a key pathophysiological mechanism in ischemic heart disease, yet current diagnosis relies on invasive pressure-wire–based measurements of the index of microcirculatory resistance (IMR) and coronary flow reserve (CFR), limiting clinical adoption. This study proposes the first data-driven, noninvasive, intra-procedural framework for quantifying CMD directly from routine coronary angiography images. Our method innovatively integrates multi-physics–informed synthetic data generation with an uncertainty-aware neural network to validate contrast intensity–time curves as reliable imaging surrogates for microcirculatory function. It combines biophysically grounded modeling, single- and dual-channel encoder–MLP architectures, and Monte Carlo Dropout for epistemic uncertainty estimation. Evaluated on physics-validated synthetic data, the model achieves high predictive accuracy, and its estimated uncertainty exhibits significant positive correlation with ground-truth prediction error. This work establishes a novel paradigm for real-time, low-cost, noninvasive CMD assessment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Coronary microvascular dysfunction (CMD), characterized by impaired regulation of blood flow in the coronary microcirculation, plays a key role in the pathogenesis of ischemic heart disease and is increasingly recognized as a contributor to adverse cardiovascular outcomes. Despite its clinical importance, CMD remains underdiagnosed due to the reliance on invasive procedures such as pressure wire-based measurements of the index of microcirculatory resistance (IMR) and coronary flow reserve (CFR), which are costly, time-consuming, and carry procedural risks. To date, no study has sought to quantify CMD indices using data-driven approaches while leveraging the rich information contained in coronary angiograms. To address these limitations, this study proposes a novel data-driven framework for inference of CMD indices based on coronary angiography. A physiologically validated multi-physics model was used to generate synthetic datasets for data-driven model training, consisting of CMD indices and computational angiograms with corresponding contrast intensity profiles (CIPs). Two neural network architectures were developed: a single-input-channel encoder-MLP model for IMR prediction and a dual-input-channel encoder-MLP model for CFR prediction, both incorporating epistemic uncertainty estimation to quantify prediction confidence. Results demonstrate that the data-driven models achieve high predictive accuracy when evaluated against physics-based synthetic datasets, and that the uncertainty estimates are positively correlated with prediction errors. Furthermore, the utility of CIPs as informative surrogates for coronary physiology is demonstrated, underscoring the potential of the proposed framework to enable accurate, real-time, image-based CMD assessment using routine angiography without the need for more invasive approaches.

Problem

• Develops data-driven methods to assess coronary microvascular dysfunction from angiograms
• Aims to replace invasive procedures for diagnosing coronary microvascular dysfunction
• Uses neural networks to predict microcirculatory indices with uncertainty estimation

Innovation

• Data-driven framework using coronary angiography for CMD assessment
• Neural networks with uncertainty estimation predict IMR and CFR
• Synthetic datasets from multi-physics model train the models