Cargando…

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

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-us...

Descripción completa

Detalles Bibliográficos
Autores principales: Benjamins, J. W., van Leeuwen, K., Hofstra, L., Rienstra, M., Appelman, Y., Nijhof, W., Verlaat, B., Everts, I., den Ruijter, H. M., Isgum, I., Leiner, T., Vliegenthart, R., Asselbergs, F. W., Juarez-Orozco, L. E., van der Harst, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bohn Stafleu van Loghum 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712143/
https://www.ncbi.nlm.nih.gov/pubmed/31111459
http://dx.doi.org/10.1007/s12471-019-1281-y
_version_ 1783446624883703808
author Benjamins, J. W.
van Leeuwen, K.
Hofstra, L.
Rienstra, M.
Appelman, Y.
Nijhof, W.
Verlaat, B.
Everts, I.
den Ruijter, H. M.
Isgum, I.
Leiner, T.
Vliegenthart, R.
Asselbergs, F. W.
Juarez-Orozco, L. E.
van der Harst, P.
author_facet Benjamins, J. W.
van Leeuwen, K.
Hofstra, L.
Rienstra, M.
Appelman, Y.
Nijhof, W.
Verlaat, B.
Everts, I.
den Ruijter, H. M.
Isgum, I.
Leiner, T.
Vliegenthart, R.
Asselbergs, F. W.
Juarez-Orozco, L. E.
van der Harst, P.
author_sort Benjamins, J. W.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-6712143
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher Bohn Stafleu van Loghum
record_format MEDLINE/PubMed
spelling pubmed-67121432019-09-13 Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium Benjamins, J. W. van Leeuwen, K. Hofstra, L. Rienstra, M. Appelman, Y. Nijhof, W. Verlaat, B. Everts, I. den Ruijter, H. M. Isgum, I. Leiner, T. Vliegenthart, R. Asselbergs, F. W. Juarez-Orozco, L. E. van der Harst, P. Neth Heart J Original Article 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. Bohn Stafleu van Loghum 2019-05-20 2019-09 /pmc/articles/PMC6712143/ /pubmed/31111459 http://dx.doi.org/10.1007/s12471-019-1281-y Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Benjamins, J. W.
van Leeuwen, K.
Hofstra, L.
Rienstra, M.
Appelman, Y.
Nijhof, W.
Verlaat, B.
Everts, I.
den Ruijter, H. M.
Isgum, I.
Leiner, T.
Vliegenthart, R.
Asselbergs, F. W.
Juarez-Orozco, L. E.
van der Harst, P.
Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium
title Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium
title_full Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium
title_fullStr Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium
title_full_unstemmed Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium
title_short Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands: CVON-AI consortium
title_sort enhancing cardiovascular artificial intelligence (ai) research in the netherlands: cvon-ai consortium
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712143/
https://www.ncbi.nlm.nih.gov/pubmed/31111459
http://dx.doi.org/10.1007/s12471-019-1281-y
work_keys_str_mv AT benjaminsjw enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT vanleeuwenk enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT hofstral enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT rienstram enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT appelmany enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT nijhofw enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT verlaatb enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT evertsi enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT denruijterhm enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT isgumi enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT leinert enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT vliegenthartr enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT asselbergsfw enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT juarezorozcole enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium
AT vanderharstp enhancingcardiovascularartificialintelligenceairesearchinthenetherlandscvonaiconsortium