TY - JOUR
T1 - Clinically-Driven Virtual Patient Cohorts Generation
T2 - An Application to Aorta
AU - Romero, Pau
AU - Lozano, Miguel
AU - Martínez-Gil, Francisco
AU - Serra, Dolors
AU - Sebastián, Rafael
AU - Lamata, Pablo
AU - García-Fernández, Ignacio
N1 - Funding Information:
This research has been partially funded by VLC-BIOMED Grant Programme, under the project 12-PARAMVALVE-GARCIA-ALBERICH-2017-B. PL holds a Wellcome Trust Senior Research Fellowships (209450/Z/17/Z).
Funding Information:
Funding. This research has been partially funded by VLC-BIOMED Grant Programme, under the project 12-PARAMVALVE-GARCIA-ALBERICH-2017-B. PL holds a Wellcome Trust Senior Research Fellowships (209450/Z/17/Z).
Publisher Copyright:
© Copyright © 2021 Romero, Lozano, Martínez-Gil, Serra, Sebastián, Lamata and García-Fernández.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it reflects enough inter-patient variability. This paper addresses the problem of generating virtual patient cohorts of thoracic aorta geometries that can be used for in-silico trials. In particular, we focus on the problem of generating a cohort of patients that meet a particular clinical criterion, regardless the access to a reference sample of that phenotype. We formalize the problem of clinically-driven sampling and assess several sampling strategies with two goals, sampling efficiency, i.e., that the generated individuals actually belong to the target population, and that the statistical properties of the cohort can be controlled. Our results show that generative adversarial networks can produce reliable, clinically-driven cohorts of thoracic aortas with good efficiency. Moreover, non-linear predictors can serve as an efficient alternative to the sometimes expensive evaluation of anatomical or functional parameters of the organ of interest.
AB - The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it reflects enough inter-patient variability. This paper addresses the problem of generating virtual patient cohorts of thoracic aorta geometries that can be used for in-silico trials. In particular, we focus on the problem of generating a cohort of patients that meet a particular clinical criterion, regardless the access to a reference sample of that phenotype. We formalize the problem of clinically-driven sampling and assess several sampling strategies with two goals, sampling efficiency, i.e., that the generated individuals actually belong to the target population, and that the statistical properties of the cohort can be controlled. Our results show that generative adversarial networks can produce reliable, clinically-driven cohorts of thoracic aortas with good efficiency. Moreover, non-linear predictors can serve as an efficient alternative to the sometimes expensive evaluation of anatomical or functional parameters of the organ of interest.
KW - clinically-driven sampling
KW - digital twin
KW - generative adversarial network
KW - in-silico trials
KW - support vector machine
KW - synthetic population
KW - thoracic-aorta
KW - virtual cohort
UR - http://www.scopus.com/inward/record.url?scp=85115010255&partnerID=8YFLogxK
U2 - 10.3389/fphys.2021.713118
DO - 10.3389/fphys.2021.713118
M3 - Article
AN - SCOPUS:85115010255
SN - 1664-042X
VL - 12
JO - Frontiers in Physiology
JF - Frontiers in Physiology
M1 - 713118
ER -