An artificial intelligence model for the simulation of visual effects in patients with visual field defects.

ABSTRACT:

Background

This study aimed to simulate the visual field (VF) effects of patients with VF defects using deep learning and computer vision technology.

Methods

We collected 3,660 Humphrey visual fields (HVFs) as data samples, including 3,263 reliable 24-2 HVFs. The convolutional neural network (CNN) analyzed and converted the grayscale map of reliable samples into structured data. The artificial intelligence (AI) simulations were developed using computer vision technology. In statistical analyses, the pilot study determined 687 reliable samples to conduct clinical trials, and the two independent sample t-tests were used to calculate the difference of the cumulative gray values. Three volunteers evaluated the matching degree of shape and position between the grayscale map and t

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC7327351 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature