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Fundus Tessellated Density Assessed by Deep Learning in Primary School Children
PURPOSE: To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning. METHODS: Comprehensive ocular examinations were conducted in 577 children age...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289270/ https://www.ncbi.nlm.nih.gov/pubmed/37342054 http://dx.doi.org/10.1167/tvst.12.6.11 |
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author | Huang, Dan Li, Rui Qian, Yingxiao Ling, Saiguang Dong, Zhou Ke, Xin Yan, Qi Tong, Haohai Wang, Zijin Long, Tengfei Liu, Hu Zhu, Hui |
author_facet | Huang, Dan Li, Rui Qian, Yingxiao Ling, Saiguang Dong, Zhou Ke, Xin Yan, Qi Tong, Haohai Wang, Zijin Long, Tengfei Liu, Hu Zhu, Hui |
author_sort | Huang, Dan |
collection | PubMed |
description | PURPOSE: To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning. METHODS: Comprehensive ocular examinations were conducted in 577 children aged 7 years old from a population-based cross-sectional study, including biometric measurement, refraction, optical coherence tomography angiography, and 45° nonmydriatic fundus photography. FTD was defined as the average exposed choroid area per unit area of the fundus, and obtained by artificial intelligence technology. The distribution of FT was classified into the macular pattern and the peripapillary pattern according to FTD. RESULTS: The mean FTD was 0.024 ± 0.026 in whole fundus. Multivariate regression analysis showed that greater FTD was significantly correlated with thinner subfoveal choroidal thickness, larger parapapillary atrophy, greater vessel density inside the optic disc, larger vertical diameter of optic disc, thinner retinal nerve fiber layer, and longer distance from optic disc center to macular fovea (all P < 0.05). The peripapillary distributed group had larger parapapillary atrophy (0.052 ± 0.119 vs 0.031 ± 0.072), greater FTD (0.029 ± 0.028 vs 0.015 ± 0.018), thinner subfoveal choroidal thickness (297.66 ± 60.61 vs 315.33 ± 66.46), and thinner retinal thickness (285.55 ± 10.89 vs 288.03 ± 10.31) than the macular distributed group (all P < 0.05). CONCLUSIONS: FTD can be applied as a quantitative biomarker to estimate subfoveal choroidal thickness in children. The role of blood flow inside optic disc in FT progression needs further investigation. The distribution of FT and the peripapillary pattern correlated more with myopia-related fundus changes than the macular pattern. TRANSLATIONAL RELEVANCE: Artificial intelligence can evaluate FT quantitatively in children, and has potential value for assisting in myopia prevention and control. |
format | Online Article Text |
id | pubmed-10289270 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-102892702023-06-24 Fundus Tessellated Density Assessed by Deep Learning in Primary School Children Huang, Dan Li, Rui Qian, Yingxiao Ling, Saiguang Dong, Zhou Ke, Xin Yan, Qi Tong, Haohai Wang, Zijin Long, Tengfei Liu, Hu Zhu, Hui Transl Vis Sci Technol Artificial Intelligence PURPOSE: To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning. METHODS: Comprehensive ocular examinations were conducted in 577 children aged 7 years old from a population-based cross-sectional study, including biometric measurement, refraction, optical coherence tomography angiography, and 45° nonmydriatic fundus photography. FTD was defined as the average exposed choroid area per unit area of the fundus, and obtained by artificial intelligence technology. The distribution of FT was classified into the macular pattern and the peripapillary pattern according to FTD. RESULTS: The mean FTD was 0.024 ± 0.026 in whole fundus. Multivariate regression analysis showed that greater FTD was significantly correlated with thinner subfoveal choroidal thickness, larger parapapillary atrophy, greater vessel density inside the optic disc, larger vertical diameter of optic disc, thinner retinal nerve fiber layer, and longer distance from optic disc center to macular fovea (all P < 0.05). The peripapillary distributed group had larger parapapillary atrophy (0.052 ± 0.119 vs 0.031 ± 0.072), greater FTD (0.029 ± 0.028 vs 0.015 ± 0.018), thinner subfoveal choroidal thickness (297.66 ± 60.61 vs 315.33 ± 66.46), and thinner retinal thickness (285.55 ± 10.89 vs 288.03 ± 10.31) than the macular distributed group (all P < 0.05). CONCLUSIONS: FTD can be applied as a quantitative biomarker to estimate subfoveal choroidal thickness in children. The role of blood flow inside optic disc in FT progression needs further investigation. The distribution of FT and the peripapillary pattern correlated more with myopia-related fundus changes than the macular pattern. TRANSLATIONAL RELEVANCE: Artificial intelligence can evaluate FT quantitatively in children, and has potential value for assisting in myopia prevention and control. The Association for Research in Vision and Ophthalmology 2023-06-21 /pmc/articles/PMC10289270/ /pubmed/37342054 http://dx.doi.org/10.1167/tvst.12.6.11 Text en Copyright 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
spellingShingle | Artificial Intelligence Huang, Dan Li, Rui Qian, Yingxiao Ling, Saiguang Dong, Zhou Ke, Xin Yan, Qi Tong, Haohai Wang, Zijin Long, Tengfei Liu, Hu Zhu, Hui Fundus Tessellated Density Assessed by Deep Learning in Primary School Children |
title | Fundus Tessellated Density Assessed by Deep Learning in Primary School Children |
title_full | Fundus Tessellated Density Assessed by Deep Learning in Primary School Children |
title_fullStr | Fundus Tessellated Density Assessed by Deep Learning in Primary School Children |
title_full_unstemmed | Fundus Tessellated Density Assessed by Deep Learning in Primary School Children |
title_short | Fundus Tessellated Density Assessed by Deep Learning in Primary School Children |
title_sort | fundus tessellated density assessed by deep learning in primary school children |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289270/ https://www.ncbi.nlm.nih.gov/pubmed/37342054 http://dx.doi.org/10.1167/tvst.12.6.11 |
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