Review in coronary functional evaluation methods: applications of computational fluid dynamics and artificial intelligence in the context of coronary artery disease.
6 affiliations
- 1College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China.
- 2Department of Cardiology, Jinhua Municipal Central Hospital, Jinhua, China.
- 3School of Control Science and Engineering, Shandong University, Jinan, China.
- 4School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China.
- 5Department of Medical Imaging, Jinhua Municipal Central Hospital, Jinhua, China.
- 6Research and Development Department, Zhejiang Demetics Medical Technology Co., Ltd., Hangzhou, China.
Abstract
Coronary artery disease, driven by the progressive accumulation of atherosclerotic plaque within the epicardial coronary arteries, remains a leading cause of myocardial ischemia and global morbidity. A fundamental challenge in the clinical management of the disease has been the reliance on anatomic stenosis severity alone, a metric that frequently dissociates from the hemodynamic significance of a lesion and thus offers limited guidance for optimal revascularization. The advent of invasive measurements marked a transformative paradigm shift, enabling the development of a diverse functional indices for the evaluation of coronary physiology. Recently, the development of computational science and artificial intelligence has accelerated the translation of these physiological assessments into the non-invasive domain, primarily through high-fidelity numerical simulations and efficient machine learning algorithms. This review systematically examines the evolution of coronary functional assessment across several pivotal frontiers. It begins by contextualizing the diagnostic landscape prior to the widespread adoption of functional indices, where clinical decisions were predominantly informed by imaging-derived anatomical parameters. Subsequently, it traces the historical evolution of invasive coronary functional indices, critically evaluating the comparative effectiveness of pressure-derived metrics against flow-derived metrics in the detection of hemodynamically significant ischemia. Furthermore, a critical evaluation of emerging artificial intelligence methodologies for the non-invasive evaluation of functional significance were employed. By synthesizing these technological methodologies, this review aims to elucidate the appropriate clinical application of each functional index, thereby providing a road-map for clinicians to achieve precise, individualized ischemia evaluation and guide therapeutic decision-making in the contemporary era of precision cardiovascular medicine.
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BibTeX
@article{qu2026review,
title = {{Review in coronary functional evaluation methods: applications of computational fluid dynamics and artificial intelligence in the context of coronary artery disease.}},
author = {Haojia Qu and Peiqi Lou and Yibin Pan and Debao Guan and Tianqi Wang and Hao Xu and Xiaorong Chen and Xinyang Ge},
journal = {Frontiers in physiology},
year = {2026},
month = oct,
doi = {10.3389/fphys.2026.1875617},
url = {https://europepmc.org/article/MED/42923534},
note = {CC BY},
}
RIS
TY - JOUR
TI - Review in coronary functional evaluation methods: applications of computational fluid dynamics and artificial intelligence in the context of coronary artery disease.
AU - Haojia Qu
AU - Peiqi Lou
AU - Yibin Pan
AU - Debao Guan
AU - Tianqi Wang
AU - Hao Xu
AU - Xiaorong Chen
AU - Xinyang Ge
PY - 2026
DA - 2026/10/08
JO - Frontiers in physiology
DO - 10.3389/fphys.2026.1875617
UR - https://europepmc.org/article/MED/42923534
AB - Coronary artery disease, driven by the progressive accumulation of atherosclerotic plaque within the epicardial coronary arteries, remains a leading cause of myocardial ischemia and global morbidity. A fundamental challenge in the clinical management of the disease has been the reliance on anatomic stenosis severity alone, a metric that frequently dissociates from the hemodynamic significance of a lesion and thus offers limited guidance for optimal revascularization. The advent of invasive measurements marked a transformative paradigm shift, enabling the development of a diverse functional indices for the evaluation of coronary physiology. Recently, the development of computational science and artificial intelligence has accelerated the translation of these physiological assessments into the non-invasive domain, primarily through high-fidelity numerical simulations and efficient machine learning algorithms. This review systematically examines the evolution of coronary functional assessment across several pivotal frontiers. It begins by contextualizing the diagnostic landscape prior to the widespread adoption of functional indices, where clinical decisions were predominantly informed by imaging-derived anatomical parameters. Subsequently, it traces the historical evolution of invasive coronary functional indices, critically evaluating the comparative effectiveness of pressure-derived metrics against flow-derived metrics in the detection of hemodynamically significant ischemia. Furthermore, a critical evaluation of emerging artificial intelligence methodologies for the non-invasive evaluation of functional significance were employed. By synthesizing these technological methodologies, this review aims to elucidate the appropriate clinical application of each functional index, thereby providing a road-map for clinicians to achieve precise, individualized ischemia evaluation and guide therapeutic decision-making in the contemporary era of precision cardiovascular medicine.
SN - 1664-042X
ER -