Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta

Pau Romero, Miguel Lozano, Francisco Martínez-Gil, Dolors Serra, Rafael Sebastián, Pablo Lamata, Ignacio García-Fernández*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number713118
JournalFrontiers in Physiology
Volume12
DOIs
Publication statusPublished - 1 Sept 2021

Keywords

  • clinically-driven sampling
  • digital twin
  • generative adversarial network
  • in-silico trials
  • support vector machine
  • synthetic population
  • thoracic-aorta
  • virtual cohort

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