Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Huang, Dan, Li, Rui, Qian, Yingxiao, Ling, Saiguang, Dong, Zhou, Ke, Xin, Yan, Qi, Tong, Haohai, Wang, Zijin, Long, Tengfei, Liu, Hu, Zhu, Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2023
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
_version_ 1785062238797692928
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
work_keys_str_mv AT huangdan fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT lirui fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT qianyingxiao fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT lingsaiguang fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT dongzhou fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT kexin fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT yanqi fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT tonghaohai fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT wangzijin fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT longtengfei fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT liuhu fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren
AT zhuhui fundustessellateddensityassessedbydeeplearninginprimaryschoolchildren