ArticleOpen heart2020
Automated interpretation of the coronary angioscopy with deep convolutional neural networks.
Article in Open heart, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- From intravascular imaging to adaptive vascular care: intelligent photonics and digital twins in panvascular disease.Light, science & applications · 2026Review
- Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.Journal of medical artificial intelligence · 2022Article
- Detection of Vulnerable Coronary Plaques Using Invasive and Non-Invasive Imaging Modalities.Journal of clinical medicine · 2022Review
- Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review.Frontiers in cardiovascular medicine · 2022Article
- Concerns in the use of adversarial learning for image synthesis in cardiovascular intervention.European heart journal. Digital health · 2021Article
- Intracoronary Thrombogenicity in Patients with Vasospastic Angina: An Observation Using Coronary Angioscopy.Diagnostics (Basel, Switzerland) · 2021Article
- Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps.Frontiers in cardiovascular medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCoronary angioscopy (CAS) is a useful modality to assess atherosclerotic changes, but interpretation of the images requires expert knowledge. Deep convolutional neural networks (DCNN) can be used for diagnostic prediction and image synthesis.
methods107 images from 47 patients, who underwent CAS in our hospital between 2014 and 2017, and 864 images, selected from 142 MEDLINE-indexed articles published between 2000 and 2019, were analysed. First, we developed a prediction model for the angioscopic findings. Next, we made a generative adversarial networks (GAN) model to simulate the CAS images. Finally, we tried to control the output images according to the angioscopic findings with conditional GAN architecture.
resultsFor both yellow colour (YC) grade and neointimal coverage (NC) grade, we could observe strong correlations between the true grades and the predicted values (YC grade, average r=0.80±0.02, p<0.001; NC grade, average r=0.73±0.02, p<0.001). The binary classification model for the red thrombus yielded 0.71±0.03 F
conclusionDCNN is useful in both predictive and generative modelling that can help develop the diagnostic support system for CAS.
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Registered trials
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