Abstract
The development of quantitative imaging analysis of the human coronary artery is an important contribution to daily clinical practice and research in cardiovascular medicine. However, cardiovascular data that are directly acquired are not identical to data acquired using imaging analysis. Whereas the accuracy and reproducibility of machine learning (ML) analysis on the lumen, vessel, and plaque areas are developed, many diverse efforts are still needed to provide the best patient care.
This is a preview of subscription content, log in via an institution to check access.
References
Suzuki N, Asano T, Nakazawa G, Aoki J, Tanabe K, Hibi K et al (2020) Clinical expert consensus document on quantitative coronary angiography from the Japanese Association of Cardiovascular Intervention and Therapeutics. Cardiovasc Interv Ther 35(2):105–116
Bass B, Garcia-Garcia H, Sanz-Sánchez J, Ziemer P, Bulantf C, Kuku K et al (2022) Human vs. Machine vs. Core Lab for the Assessment of Coronary Atherosclerosis with Lumen and Vessel Contour Segmentation with Intravascular Ultrasound.Int J Cardio Imaging. 2022:(in press).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Suzuki, N. Title: how accurately can machine learning technology for quantitative imaging analysis represent actual coronary artery data?. Int J Cardiovasc Imaging 38, 1441–1442 (2022). https://doi.org/10.1007/s10554-022-02575-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10554-022-02575-2