Automated interpretation of the coronary angioscopy with deep convolutional neural networks.

ABSTRACT:

Background

Coronary 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.

Methods

107 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.

Results

For both yellow colour (YC) grade and

SUBMITTER: Miyoshi T 

PROVIDER: S-EPMC7228653 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature