Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance

Yili Feng; Ruisen Fu; Hao Sun; Xue Wang; Yang Yang; Chuanqi Wen; Yaodong Hao; Yutong Sun; Bao Li; Na Li; Haisheng Yang; Quansheng Feng; Jian Liu; Zhuo Liu; Liyuan Zhang; Youjun Liu

doi:10.1016/j.artmed.2023.102744

Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance

Artif Intell Med. 2024 Jan:147:102744. doi: 10.1016/j.artmed.2023.102744. Epub 2023 Dec 5.

Authors

Yili Feng¹, Ruisen Fu¹, Hao Sun¹, Xue Wang², Yang Yang¹, Chuanqi Wen¹, Yaodong Hao¹, Yutong Sun³, Bao Li¹, Na Li⁴, Haisheng Yang¹, Quansheng Feng⁵, Jian Liu³, Zhuo Liu⁶, Liyuan Zhang⁷, Youjun Liu⁸

Affiliations

¹ Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
² Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Department of Equipment and Materials, Tianjin First Central Hospital, Tianjin, China.
³ Department of Cardiology, Peking University People's Hospital, Beijing, China.
⁴ Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, Shandong 271016, China.
⁵ Department of Cardiology, The First People's Hospital of Guangshui, Hubei 432700, China.
⁶ Department of Radiology, Peking University People's Hospital, Beijing, China.
⁷ Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China. Electronic address: LiyuanZhang@bjut.edu.cn.
⁸ Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China. Electronic address: lyjlma@bjut.edu.cn.

PMID: 38184351
DOI: 10.1016/j.artmed.2023.102744

Abstract

Background and objective: Recently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis.

Methods: A reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis. Importantly, the LPM was personalized in both structures and parameters according to coronary geometries from computed tomography angiography and physiological measurements such as blood pressure and cardiac output for personalized simulations of coronary pressure and flow. Coronary lesions with invasive FFR ≤ 0.80 were defined as hemodynamically significant.

Results: A total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFR_ML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFR_ML simulation was about 10 min. On a per-vessel basis, the FFR_ML and FFR were significantly correlated (r = 0.86, p < 0.001). The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 91.4%, 92.6%, 90.9%, 80.6% and 96.8%, respectively. The area under the receiver-operating characteristic curve of FFR_ML was 0.984.

Conclusion: In this selected cohort of patients, the FFR_ML improves the computational efficiency and ensures the accuracy. The favorable performance of FFR_ML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.

Keywords: Fractional flow reserve; Machine learning; Numerical simulation; Reduced-order model; Stenotic flow resistance.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Blood Pressure
Computed Tomography Angiography
Constriction, Pathologic
Fractional Flow Reserve, Myocardial*
Humans
Machine Learning