Journal
Frontiers in Physiology
Publication Date
1-1-2022
Volume
13
First Page
912447
Document Type
Open Access Publication
DOI
10.3389/fphys.2022.912447
Rights and Permissions
Guo X, Maehara A, Yang M, Wang L, Zheng J, Samady H, Mintz GS, Giddens DP and Tang D (2022) Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach. Front. Physiol. 13:912447. doi: 10.3389/fphys.2022.912447 © 2022 Guo, Maehara, Yang, Wang, Zheng, Samady, Mintz, Giddens and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Recommended Citation
Guo, Xiaoya; Maehara, Akiko; Yang, Mingming; Wang, Liang; Zheng, Jie; Samady, Habib; Mintz, Gary S; Giddens, Don P; and Tang, Dalin, "Predicting coronary stenosis progression using plaque fatigue from IVUS-based thin-slice models: A machine learning random forest approach." Frontiers in Physiology. 13, 912447 (2022).
https://digitalcommons.wustl.edu/oa_4/1577
Department
ICTS (Institute of Clinical and Translational Sciences)
