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An artificial intelligence model for the simulation of visual effects in patients with visual field defects
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)...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7327351/ https://www.ncbi.nlm.nih.gov/pubmed/32617323 http://dx.doi.org/10.21037/atm.2020.02.162 |
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author | Zhou, Zhan Li, Bingbing Su, Jinyu Fan, Xianming Chen, Liang Tang, Song Zheng, Jianqing Zhang, Tong Meng, Zhiyong Chen, Zhimeng Deng, Hongwei Hu, Jianmin Zhao, Jun |
author_facet | Zhou, Zhan Li, Bingbing Su, Jinyu Fan, Xianming Chen, Liang Tang, Song Zheng, Jianqing Zhang, Tong Meng, Zhiyong Chen, Zhimeng Deng, Hongwei Hu, Jianmin Zhao, Jun |
author_sort | Zhou, Zhan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7327351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-73273512020-07-01 An artificial intelligence model for the simulation of visual effects in patients with visual field defects Zhou, Zhan Li, Bingbing Su, Jinyu Fan, Xianming Chen, Liang Tang, Song Zheng, Jianqing Zhang, Tong Meng, Zhiyong Chen, Zhimeng Deng, Hongwei Hu, Jianmin Zhao, Jun Ann Transl Med Original Article on Medical Artificial Intelligent Research 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. AME Publishing Company 2020-06 /pmc/articles/PMC7327351/ /pubmed/32617323 http://dx.doi.org/10.21037/atm.2020.02.162 Text en 2020 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article on Medical Artificial Intelligent Research Zhou, Zhan Li, Bingbing Su, Jinyu Fan, Xianming Chen, Liang Tang, Song Zheng, Jianqing Zhang, Tong Meng, Zhiyong Chen, Zhimeng Deng, Hongwei Hu, Jianmin Zhao, Jun An artificial intelligence model for the simulation of visual effects in patients with visual field defects |
title | An artificial intelligence model for the simulation of visual effects in patients with visual field defects |
title_full | An artificial intelligence model for the simulation of visual effects in patients with visual field defects |
title_fullStr | An artificial intelligence model for the simulation of visual effects in patients with visual field defects |
title_full_unstemmed | An artificial intelligence model for the simulation of visual effects in patients with visual field defects |
title_short | An artificial intelligence model for the simulation of visual effects in patients with visual field defects |
title_sort | artificial intelligence model for the simulation of visual effects in patients with visual field defects |
topic | Original Article on Medical Artificial Intelligent Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7327351/ https://www.ncbi.nlm.nih.gov/pubmed/32617323 http://dx.doi.org/10.21037/atm.2020.02.162 |
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