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Angiography-based coronary flow reserve: The feasibility of automatic computation by artificial intelligence
Affiliations- Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Med-X Engineering Research Center, Shanghai Jiao Tong University, Shanghai, China
- Cardiology Department, Campo de Gibraltar Health Trust, Algeciras, Spain
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Abstract
Background: Coronary flow reserve (CFR) has prognostic value in patients with coronary artery disease.
However, its measurement is complex, and automatic methods for CFR computation are scarcely
available. We developed an automatic method for CFR computation based on coronary angiography and
assessed its feasibility.
Methods: Coronary angiographies from the Corelab database were annotated by experienced analysts.
A convolutional neural network (CNN) model was trained for automatic segmentation of the main
coronary arteries during contrast injection. The segmentation performance was evaluated using 5-fold
cross-validation. Subsequently, the CNN model was implemented into a prototype software package for
automatic computation of the CFR (CFRauto) and applied on a different sample of patients with angiographies
performed both at rest and during maximal hyperemia, to assess the feasibility of CFRauto and
its agreement with the manual computational method based on frame count (CFRmanual).
Results: Altogether, 137,126 images of 5913 angiographic runs from 2407 patients were used to
develop and evaluate the CNN model. Good segmentation performance was observed. CFRauto was successfully
computed in 136 out of 149 vessels (91.3%). The average analysis time to derive CFRauto was
18.1 ± 10.3 s per vessel. Moderate correlation (r = 0.51, p < 0.001) was observed between CFRauto and
CFRmanual, with a mean difference of 0.12 ± 0.53.
Conclusions: Automatic computation of the CFR based on coronary angiography is feasible. This
method might facilitate wider adoption of coronary physiology in the catheterization laboratory to assess
microcirculatory function.
Abstract
Background: Coronary flow reserve (CFR) has prognostic value in patients with coronary artery disease.
However, its measurement is complex, and automatic methods for CFR computation are scarcely
available. We developed an automatic method for CFR computation based on coronary angiography and
assessed its feasibility.
Methods: Coronary angiographies from the Corelab database were annotated by experienced analysts.
A convolutional neural network (CNN) model was trained for automatic segmentation of the main
coronary arteries during contrast injection. The segmentation performance was evaluated using 5-fold
cross-validation. Subsequently, the CNN model was implemented into a prototype software package for
automatic computation of the CFR (CFRauto) and applied on a different sample of patients with angiographies
performed both at rest and during maximal hyperemia, to assess the feasibility of CFRauto and
its agreement with the manual computational method based on frame count (CFRmanual).
Results: Altogether, 137,126 images of 5913 angiographic runs from 2407 patients were used to
develop and evaluate the CNN model. Good segmentation performance was observed. CFRauto was successfully
computed in 136 out of 149 vessels (91.3%). The average analysis time to derive CFRauto was
18.1 ± 10.3 s per vessel. Moderate correlation (r = 0.51, p < 0.001) was observed between CFRauto and
CFRmanual, with a mean difference of 0.12 ± 0.53.
Conclusions: Automatic computation of the CFR based on coronary angiography is feasible. This
method might facilitate wider adoption of coronary physiology in the catheterization laboratory to assess
microcirculatory function.
Keywords
artificial intelligence, convolutional network, coronary flow reserve, X-ray angiography, coronary heart disease
About this articleTitle
Angiography-based coronary flow reserve: The feasibility of automatic computation by artificial intelligence
Journal
Issue
Article type
Original Article
Pages
369-378
Published online
2021-08-02
Page views
2892
Article views/downloads
919
DOI
Pubmed
Bibliographic record
Cardiol J 2023;30(3):369-378.
Keywords
artificial intelligence
convolutional network
coronary flow reserve
X-ray angiography
coronary heart disease
Authors
Qiuyang Zhao
Chunming Li
Miao Chu
Juan Luis Gutiérrez-Chico
Shengxian Tu
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