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

Zhan Zhou; Bingbing Li; Jinyu Su; Xianming Fan; Liang Chen; Song Tang; Jianqing Zheng; Tong Zhang; Zhiyong Meng; Zhimeng Chen; Hongwei Deng; Jianmin Hu; Jun Zhao

doi:10.21037/atm.2020.02.162

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

Ann Transl Med. 2020 Jun;8(11):703. doi: 10.21037/atm.2020.02.162.

Authors

Zhan Zhou¹, Bingbing Li², Jinyu Su², Xianming Fan¹, Liang Chen¹, Song Tang¹, Jianqing Zheng², Tong Zhang¹, Zhiyong Meng², Zhimeng Chen², Hongwei Deng¹, Jianmin Hu², Jun Zhao^{1

3}

Affiliations

¹ Shenzhen Eye Hospital Affiliated to Jinan University, Shenzhen Eye Institute, Shenzhen, China.
² The Second Affiliated Hospital of Fujian Medical University, Fujian Province University Engineering Research Center of Assistive Technology for Visual Impairment, Quanzhou, China.
³ School of Optometry Affiliated to Shenzhen University, Shenzhen, China.

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 the AI simulation, which was graded from 0 to100 scores. Based on the average ranking, the proportion of good and excellent grades was determined, and thus the reliability of the AI simulations was assessed.

Results: The reliable samples in the experimental data consisted of 1,334 normal samples and 1,929 abnormal samples. Based on the existing mature CNN model, the fully connected layer was integrated to analyze the VF damage parameters of the input images, and the prediction accuracy of the damage type of the VF defects was up to 89%. By mapping the area and damage information in the VF damage parameter quintuple data set into the real scene image and adjusting the darkening effect according to the damage parameter, the visual effects in patients were simulated in the real scene image. In the clinical validation, there was no statistically significant difference in the cumulative gray value (P>0.05). The good and excellent proportion of the average scores reached 96.0%, thus confirming the accuracy of the AI model.

Conclusions: An AI model with high accuracy was established to simulate the visual effects in patients with VF defects.

Keywords: Computer vision technology; artificial intelligence (AI); visual field defects; visual simulation.