An AI-based algorithmic system that predicts missing A-scans in cross-sectional retinal images

Abstract

In this study, we present an artificial intelligence based algorithmic system that predicts missing A-scans of the edited OCT image by padding the A scan with zero. The developed artificial intelligence algorithmic system consists of two networks: convolutional neural network and generative adversarial network. The system theoretically suggests that skipping one-third of sequential A-scans and predicting the missing A-scan can increase the image acquisition rate by at least 33%. The structural similarity index measurement of the test data reaches an average of 82% between the ground truth images and the images predicted from the developed system. The mean squared error also is to 0.2%.