TY - JOUR
T1 - Enhancing cardiovascular artificial intelligence (AI) research in the Netherlands
T2 - CVON-AI consortium
AU - Benjamins, J. W.
AU - van Leeuwen, K.
AU - Hofstra, L.
AU - Rienstra, M.
AU - Appelman, Y.
AU - Nijhof, W.
AU - Verlaat, B.
AU - Everts, I.
AU - den Ruijter, H. M.
AU - Isgum, I.
AU - Leiner, T.
AU - Vliegenthart, R.
AU - Asselbergs, F. W.
AU - Juarez-Orozco, L. E.
AU - van der Harst, P.
N1 - Funding Information: The Research Project CVON-AI (2018B017) is financed by the PPP Allowance made available by Top Sector Life Sciences & Health to the Nederlandse Hartstichting to stimulate public-private partnerships. This work reflects only the authors? view, not that of the funders. Stichting LSH-TKI or Hartstichting or the Ministry of Economic Affairs is not responsible for any use that may be made of the information it contains. Folkert W.?Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre. Funding Information: The Research Project CVON-AI (2018B017) is financed by the PPP Allowance made available by Top Sector Life Sciences & Health to the Nederlandse Hartstichting to stimulate public-private partnerships. This work reflects only the authors’ view, not that of the funders. Stichting LSH-TKI or Hartstichting or the Ministry of Economic Affairs is not responsible for any use that may be made of the information it contains. Folkert W. Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre. Publisher Copyright: © 2019, The Author(s).
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - CVON-AI consortium
KW - Cardiovascular disease
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85071291867&partnerID=8YFLogxK
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85071291867&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/31111459
U2 - https://doi.org/10.1007/s12471-019-1281-y
DO - https://doi.org/10.1007/s12471-019-1281-y
M3 - Article
C2 - 31111459
SN - 1568-5888
VL - 27
SP - 414
EP - 425
JO - Netherlands heart journal
JF - Netherlands heart journal
IS - 9
ER -