This paper is in the following e-collection/theme issue:

Short Paper (29) Virtual Patients (62) Artificial Intelligence (1831) Medical imaging (74) Imaging Informatics (250) Applications of AI (192)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63130, first published .
Generating Artificial Patients With Reliable Clinical Characteristics Using a Geometry-Based Variational Autoencoder: Proof-of-Concept Feasibility Study

Generating Artificial Patients With Reliable Clinical Characteristics Using a Geometry-Based Variational Autoencoder: Proof-of-Concept Feasibility Study

Generating Artificial Patients With Reliable Clinical Characteristics Using a Geometry-Based Variational Autoencoder: Proof-of-Concept Feasibility Study

Authors of this article:

Fabrice Ferré1 Author Orcid Image ;   Stéphanie Allassonnière2 Author Orcid Image ;   Clément Chadebec2 Author Orcid Image ;   Vincent Minville1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Fabrice Ferré   1 , MD, PhD ;   Stéphanie Allassonnière   2 , PhD ;   Clément Chadebec   2 , PhD ;   Vincent Minville   1 , MD, PhD

1 Department of Anesthesia, Intensive Care and Perioperative Medicine, Purpan University Hospital, Toulouse, France

2 Université Paris Cité, Unité Mixte de Recherche S1138, Institut national de recherche en sciences et technologies du numérique, Sorbonne University, Paris, France

Corresponding Author:

  • Fabrice Ferré, MD, PhD
  • Department of Anesthesia, Intensive Care and Perioperative Medicine
  • Purpan University Hospital
  • Place du Dr Baylac
  • Toulouse 31300
  • France
  • Phone: 33 561779988
  • Email: fabriceferre31@gmail.com