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Non-Invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-driven or Model-based?

Authors :
Maxime Sermesant
Yingyu Yang
Pamela Moceri
Stephane Gillon
Jaume Banus
Inria Sophia Antipolis - Méditerranée (CRISAM)
Institut National de Recherche en Informatique et en Automatique (Inria)
Hôpital Pasteur [Nice] (CHU)
E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)
YANG, Yingyu
3IA Côte d'Azur - - 3IA@cote d'azur2019 - ANR-19-P3IA-0002 - P3IA - VALID
Source :
STACOM 2019-10th Workshop on Statistical Atlases and Computational Modelling of the Heart, STACOM 2019-10th Workshop on Statistical Atlases and Computational Modelling of the Heart, Oct 2019, Shenzhen, China, Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges ISBN: 9783030390730, STACOM@MICCAI
Publication Year :
2019
Publisher :
HAL CCSD, 2019.

Abstract

International audience; Right heart catheterisation is considered as the gold standard for the assessment of patients with suspected pulmonary hyper-tension. It provides clinicians with meaningful data, such as pulmonary capillary wedge pressure and pulmonary vascular resistance, however its usage is limited due to its invasive nature. Non-invasive alternatives, like Doppler echocardiography could present insightful measurements of right heart but lack detailed information related to pulmonary vascu-lature. In order to explore non-invasive means, we studied a dataset of 95 pulmonary hypertension patients, which includes measurements from echocardiography and from right-heart catheterisation. We used data extracted from echocardiography to conduct cardiac circulation model per-sonalisation and tested its prediction power of catheter data. Standard machine learning methods were also investigated for pulmonary artery pressure prediction. Our preliminary results demonstrated the potential prediction power of both data-driven and model-based approaches.

Details

Language :
English
ISBN :
978-3-030-39073-0
ISBNs :
9783030390730
Database :
OpenAIRE
Journal :
STACOM 2019-10th Workshop on Statistical Atlases and Computational Modelling of the Heart, STACOM 2019-10th Workshop on Statistical Atlases and Computational Modelling of the Heart, Oct 2019, Shenzhen, China, Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges ISBN: 9783030390730, STACOM@MICCAI
Accession number :
edsair.doi.dedup.....e886a7a50f2e9b52e2c6e1408f67b517