Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease

Mitchel A. Molenaar; Jasper L. Selder; Johny Nicolas; Bimmer E. Claessen; Roxana Mehran; Javier Oliván Bescós; Mark J. Schuuring; Berto J. Bouma; Niels J. Verouden; Steven A. J. Chamuleau

doi:10.1007/s11886-022-01655-y

Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease

Mitchel A. Molenaar^*
, Jasper L. Selder
, Johny Nicolas
, Bimmer E. Claessen
, Roxana Mehran
, Javier Oliván Bescós
, Mark J. Schuuring
, Berto J. Bouma
, Niels J. Verouden
, Steven A. J. Chamuleau

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

27 Citations (Scopus)

15 Downloads (Pure)

Abstract

Purpose of Review
Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA).

Recent Findings
Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31–14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated.

Summary
Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.

Original language	English
Pages (from-to)	365–376
Number of pages	12
Journal	Current Cardiology Reports
Volume	24
Issue number	4
Early online date	28 Mar 2022
DOIs	https://doi.org/10.1007/s11886-022-01655-y
Publication status	Published - Apr 2022
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Coronary angiography
Coronary stenosis
Artificial intelligence
Deep learning
Image processing

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10.1007/s11886-022-01655-yLicence: CC BY

ArticleFinal published version, 2.03 MBLicence: CC BY

Cite this

Molenaar, M. A., Selder, J. L., Nicolas, J., Claessen, B. E., Mehran, R., Oliván Bescós, J., Schuuring, M. J., Bouma, B. J., Verouden, N. J., & Chamuleau, S. A. J. (2022). Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease. Current Cardiology Reports, 24(4), 365–376. https://doi.org/10.1007/s11886-022-01655-y

@article{051cb0b61a3f409cbc0799a4e36eedd5,

title = "Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease",

abstract = "Purpose of Review Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). Recent Findings Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31–14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Summary Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.",

keywords = "Coronary angiography, Coronary stenosis, Artificial intelligence, Deep learning, Image processing",

author = "Molenaar, \{Mitchel A.\} and Selder, \{Jasper L.\} and Johny Nicolas and Claessen, \{Bimmer E.\} and Roxana Mehran and \{Oliv{\'a}n Besc{\'o}s\}, Javier and Schuuring, \{Mark J.\} and Bouma, \{Berto J.\} and Verouden, \{Niels J.\} and Chamuleau, \{Steven A. J.\}",

year = "2022",

month = apr,

doi = "10.1007/s11886-022-01655-y",

language = "English",

volume = "24",

pages = "365–376",

journal = "Current Cardiology Reports",

issn = "1523-3782",

publisher = "Springer",

number = "4",

}

Molenaar, MA, Selder, JL, Nicolas, J, Claessen, BE, Mehran, R, Oliván Bescós, J, Schuuring, MJ, Bouma, BJ, Verouden, NJ & Chamuleau, SAJ 2022, 'Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease', Current Cardiology Reports, vol. 24, no. 4, pp. 365–376. https://doi.org/10.1007/s11886-022-01655-y

Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease. / Molenaar, Mitchel A.; Selder, Jasper L.; Nicolas, Johny et al.
In: Current Cardiology Reports, Vol. 24, No. 4, 04.2022, p. 365–376.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease

AU - Molenaar, Mitchel A.

AU - Selder, Jasper L.

AU - Nicolas, Johny

AU - Claessen, Bimmer E.

AU - Mehran, Roxana

AU - Oliván Bescós, Javier

AU - Schuuring, Mark J.

AU - Bouma, Berto J.

AU - Verouden, Niels J.

AU - Chamuleau, Steven A. J.

PY - 2022/4

Y1 - 2022/4

N2 - Purpose of Review Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). Recent Findings Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31–14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Summary Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.

AB - Purpose of Review Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). Recent Findings Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31–14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Summary Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.

KW - Coronary angiography

KW - Coronary stenosis

KW - Artificial intelligence

KW - Deep learning

KW - Image processing

U2 - 10.1007/s11886-022-01655-y

DO - 10.1007/s11886-022-01655-y

M3 - Article

SN - 1523-3782

VL - 24

SP - 365

EP - 376

JO - Current Cardiology Reports

JF - Current Cardiology Reports

IS - 4

ER -

Current State and Future Perspectives of Artificial Intelligence for Automated Coronary Angiography Imaging Analysis in Patients with Ischemic Heart Disease

Abstract

UN SDGs

Keywords

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