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Neth Heart J. 2019 May 20. doi: 10.1007/s12471-019-1281-y. [Epub ahead of print]

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

Author information

1
University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands.
2
Go Data Driven, Amsterdam, The Netherlands.
3
Cardiologie Centra Nederland B.V., Utrecht, The Netherlands.
4
Department of Cardiology, Amsterdam Universities Medical Centre, location VU Medical Centre, Amsterdam, The Netherlands.
5
Siemens Healthcare Nederland B.V., Den Haag, The Netherlands.
6
Binx.io B.V., Amsterdam, The Netherlands.
7
Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.
8
University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, The Netherlands.
9
Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands.
10
Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK.
11
Institute of Health Informatics, University College London, London, UK.
12
Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland.
13
University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen, The Netherlands. p.van.der.harst@umcg.nl.
14
Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht, The Netherlands. p.van.der.harst@umcg.nl.
15
University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands. p.van.der.harst@umcg.nl.

Abstract

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:

To lay out the theoretical framework, purpose, and structure of a novel AI consortium.

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:

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:

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.

KEYWORDS:

Artificial intelligence; CVON-AI consortium; Cardiovascular disease; Machine learning

PMID:
31111459
DOI:
10.1007/s12471-019-1281-y

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