Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium.

Artificial intelligence CVON-AI consortium Cardiovascular disease Machine learning

Journal

Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation
ISSN: 1568-5888
Titre abrégé: Neth Heart J
Pays: Netherlands
ID NLM: 101095458

Informations de publication

Date de publication:
Sep 2019
Historique:
pubmed: 22 5 2019
medline: 22 5 2019
entrez: 22 5 2019
Statut: ppublish

Résumé

Machine learning (ML) allows the exploration and progressive improvement of very complex high-dimensional data patterns that can be utilised to optimise specific classification and prediction tasks, outperforming traditional statistical approaches. An enormous acceleration of ready-to-use tools and artificial intelligence (AI) applications, shaped by the emergence, refinement, and application of powerful ML algorithms in several areas of knowledge, is ongoing. Although such progress has begun to permeate the medical sciences and clinical medicine, implementation in cardiovascular medicine and research is still in its infancy. To lay out the theoretical framework, purpose, and structure of a novel AI consortium. We have established a new Dutch research consortium, the CVON-AI, supported by the Netherlands Heart Foundation, to catalyse and facilitate the development and utilisation of AI solutions for existing and emerging cardiovascular research initiatives and to raise AI awareness in the cardiovascular research community. CVON-AI will connect to previously established CVON consortia and apply a cloud-based AI platform to supplement their planned traditional data-analysis approach. A pilot experiment on the CVON-AI cloud was conducted using cardiac magnetic resonance data. It demonstrated the feasibility of the platform and documented excellent correlation between AI-generated ventricular function estimates as compared to expert manual annotations. The resulting AI solution was then integrated in a web application. CVON-AI is a new consortium meant to facilitate the implementation and raise awareness of AI in cardiovascular research in the Netherlands. CVON-AI will create an accessible cloud-based platform for cardiovascular researchers, demonstrate the clinical applicability of AI, optimise the analytical methodology of other ongoing CVON consortia, and promote AI awareness through education and training.

Sections du résumé

BACKGROUND BACKGROUND
Machine learning (ML) allows the exploration and progressive improvement of very complex high-dimensional data patterns that can be utilised to optimise specific classification and prediction tasks, outperforming traditional statistical approaches. An enormous acceleration of ready-to-use tools and artificial intelligence (AI) applications, shaped by the emergence, refinement, and application of powerful ML algorithms in several areas of knowledge, is ongoing. Although such progress has begun to permeate the medical sciences and clinical medicine, implementation in cardiovascular medicine and research is still in its infancy.
OBJECTIVES OBJECTIVE
To lay out the theoretical framework, purpose, and structure of a novel AI consortium.
METHODS METHODS
We have established a new Dutch research consortium, the CVON-AI, supported by the Netherlands Heart Foundation, to catalyse and facilitate the development and utilisation of AI solutions for existing and emerging cardiovascular research initiatives and to raise AI awareness in the cardiovascular research community. CVON-AI will connect to previously established CVON consortia and apply a cloud-based AI platform to supplement their planned traditional data-analysis approach.
RESULTS RESULTS
A pilot experiment on the CVON-AI cloud was conducted using cardiac magnetic resonance data. It demonstrated the feasibility of the platform and documented excellent correlation between AI-generated ventricular function estimates as compared to expert manual annotations. The resulting AI solution was then integrated in a web application.
CONCLUSION CONCLUSIONS
CVON-AI is a new consortium meant to facilitate the implementation and raise awareness of AI in cardiovascular research in the Netherlands. CVON-AI will create an accessible cloud-based platform for cardiovascular researchers, demonstrate the clinical applicability of AI, optimise the analytical methodology of other ongoing CVON consortia, and promote AI awareness through education and training.

Identifiants

pubmed: 31111459
doi: 10.1007/s12471-019-1281-y
pii: 10.1007/s12471-019-1281-y
pmc: PMC6712143
doi:

Types de publication

Journal Article

Langues

eng

Pagination

414-425

Subventions

Organisme : Hartstichting
ID : 2018B017

Références

Cardiovasc Drugs Ther. 2012 Oct;26(5):417-26
pubmed: 22968678
Circulation. 2015 Nov 17;132(20):1920-30
pubmed: 26572668
N Engl J Med. 2016 Sep 29;375(13):1216-9
pubmed: 27682033
JAMA. 2016 Dec 13;316(22):2402-2410
pubmed: 27898976
Nature. 2017 Feb 2;542(7639):115-118
pubmed: 28117445
Nature. 2017 Oct 18;550(7676):354-359
pubmed: 29052630
J Nucl Cardiol. 2018 May 22;:null
pubmed: 29790017

Auteurs

J W Benjamins (JW)

University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands.

K van Leeuwen (K)

Go Data Driven, Amsterdam, The Netherlands.

L Hofstra (L)

Cardiologie Centra Nederland B.V., Utrecht, The Netherlands.
Department of Cardiology, Amsterdam Universities Medical Centre, location VU Medical Centre, Amsterdam, The Netherlands.

M Rienstra (M)

University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands.

Y Appelman (Y)

Department of Cardiology, Amsterdam Universities Medical Centre, location VU Medical Centre, Amsterdam, The Netherlands.

W Nijhof (W)

Siemens Healthcare Nederland B.V., Den Haag, The Netherlands.

B Verlaat (B)

Binx.io B.V., Amsterdam, The Netherlands.

I Everts (I)

Go Data Driven, Amsterdam, The Netherlands.

H M den Ruijter (HM)

Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.

I Isgum (I)

Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.

T Leiner (T)

Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.

R Vliegenthart (R)

University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, The Netherlands.

F W Asselbergs (FW)

Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands.
Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.
Institute of Health Informatics, University College London, London, UK.

L E Juarez-Orozco (LE)

University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands.
Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland.

P van der Harst (P)

University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands. p.van.der.harst@umcg.nl.
Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands. p.van.der.harst@umcg.nl.
University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands. p.van.der.harst@umcg.nl.

Classifications MeSH