Cargando…

Morphological characteristics of retinal vessels in eyes with high myopia: Ultra-wide field images analyzed by artificial intelligence using a transfer learning system

PURPOSE: The purpose of this study is to investigate the retinal vascular morphological characteristics in high myopia patients of different severity. METHODS: 317 eyes of high myopia patients and 104 eyes of healthy control subjects were included in this study. The severity of high myopia patients...

Descripción completa

Detalles Bibliográficos
Autores principales: Mao, Jianbo, Deng, Xinyi, Ye, Yu, Liu, Hui, Fang, Yuyan, Zhang, Zhengxi, Chen, Nuo, Sun, Mingzhai, Shen, Lijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982751/
https://www.ncbi.nlm.nih.gov/pubmed/36874950
http://dx.doi.org/10.3389/fmed.2022.956179
_version_ 1784900392400715776
author Mao, Jianbo
Deng, Xinyi
Ye, Yu
Liu, Hui
Fang, Yuyan
Zhang, Zhengxi
Chen, Nuo
Sun, Mingzhai
Shen, Lijun
author_facet Mao, Jianbo
Deng, Xinyi
Ye, Yu
Liu, Hui
Fang, Yuyan
Zhang, Zhengxi
Chen, Nuo
Sun, Mingzhai
Shen, Lijun
author_sort Mao, Jianbo
collection PubMed
description PURPOSE: The purpose of this study is to investigate the retinal vascular morphological characteristics in high myopia patients of different severity. METHODS: 317 eyes of high myopia patients and 104 eyes of healthy control subjects were included in this study. The severity of high myopia patients is classified into C0–C4 according to the Meta Analysis of the Pathologic Myopia (META-PM) classification and their vascular morphological characteristics in ultra-wide field imaging were analyzed using transfer learning methods and RU-net. Correlation with axial length (AL), best corrected visual acuity (BCVA) and age was analyzed. In addition, the vascular morphological characteristics of myopic choroidal neovascularization (mCNV) patients and their matched high myopia patients were compared. RESULTS: The RU-net and transfer learning system of blood vessel segmentation had an accuracy of 98.24%, a sensitivity of 71.42%, a specificity of 99.37%, a precision of 73.68% and a F1 score of 72.29. Compared with healthy control group, high myopia group had smaller vessel angle (31.12 ± 2.27 vs. 32.33 ± 2.14), smaller fractal dimension (Df) (1.383 ± 0.060 vs. 1.424 ± 0.038), smaller vessel density (2.57 ± 0.96 vs. 3.92 ± 0.93) and fewer vascular branches (201.87 ± 75.92 vs. 271.31 ± 67.37), all P < 0.001. With the increase of myopia maculopathy severity, vessel angle, Df, vessel density and vascular branches significantly decreased (all P < 0.001). There were significant correlations of these characteristics with AL, BCVA and age. Patients with mCNV tended to have larger vessel density (P < 0.001) and more vascular branches (P = 0.045). CONCLUSION: The RU-net and transfer learning technology used in this study has an accuracy of 98.24%, thus has good performance in quantitative analysis of vascular morphological characteristics in Ultra-wide field images. Along with the increase of myopic maculopathy severity and the elongation of eyeball, vessel angle, Df, vessel density and vascular branches decreased. Myopic CNV patients have larger vessel density and more vascular branches.
format Online
Article
Text
id pubmed-9982751
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-99827512023-03-04 Morphological characteristics of retinal vessels in eyes with high myopia: Ultra-wide field images analyzed by artificial intelligence using a transfer learning system Mao, Jianbo Deng, Xinyi Ye, Yu Liu, Hui Fang, Yuyan Zhang, Zhengxi Chen, Nuo Sun, Mingzhai Shen, Lijun Front Med (Lausanne) Medicine PURPOSE: The purpose of this study is to investigate the retinal vascular morphological characteristics in high myopia patients of different severity. METHODS: 317 eyes of high myopia patients and 104 eyes of healthy control subjects were included in this study. The severity of high myopia patients is classified into C0–C4 according to the Meta Analysis of the Pathologic Myopia (META-PM) classification and their vascular morphological characteristics in ultra-wide field imaging were analyzed using transfer learning methods and RU-net. Correlation with axial length (AL), best corrected visual acuity (BCVA) and age was analyzed. In addition, the vascular morphological characteristics of myopic choroidal neovascularization (mCNV) patients and their matched high myopia patients were compared. RESULTS: The RU-net and transfer learning system of blood vessel segmentation had an accuracy of 98.24%, a sensitivity of 71.42%, a specificity of 99.37%, a precision of 73.68% and a F1 score of 72.29. Compared with healthy control group, high myopia group had smaller vessel angle (31.12 ± 2.27 vs. 32.33 ± 2.14), smaller fractal dimension (Df) (1.383 ± 0.060 vs. 1.424 ± 0.038), smaller vessel density (2.57 ± 0.96 vs. 3.92 ± 0.93) and fewer vascular branches (201.87 ± 75.92 vs. 271.31 ± 67.37), all P < 0.001. With the increase of myopia maculopathy severity, vessel angle, Df, vessel density and vascular branches significantly decreased (all P < 0.001). There were significant correlations of these characteristics with AL, BCVA and age. Patients with mCNV tended to have larger vessel density (P < 0.001) and more vascular branches (P = 0.045). CONCLUSION: The RU-net and transfer learning technology used in this study has an accuracy of 98.24%, thus has good performance in quantitative analysis of vascular morphological characteristics in Ultra-wide field images. Along with the increase of myopic maculopathy severity and the elongation of eyeball, vessel angle, Df, vessel density and vascular branches decreased. Myopic CNV patients have larger vessel density and more vascular branches. Frontiers Media S.A. 2023-02-16 /pmc/articles/PMC9982751/ /pubmed/36874950 http://dx.doi.org/10.3389/fmed.2022.956179 Text en Copyright © 2023 Mao, Deng, Ye, Liu, Fang, Zhang, Chen, Sun and Shen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Mao, Jianbo
Deng, Xinyi
Ye, Yu
Liu, Hui
Fang, Yuyan
Zhang, Zhengxi
Chen, Nuo
Sun, Mingzhai
Shen, Lijun
Morphological characteristics of retinal vessels in eyes with high myopia: Ultra-wide field images analyzed by artificial intelligence using a transfer learning system
title Morphological characteristics of retinal vessels in eyes with high myopia: Ultra-wide field images analyzed by artificial intelligence using a transfer learning system
title_full Morphological characteristics of retinal vessels in eyes with high myopia: Ultra-wide field images analyzed by artificial intelligence using a transfer learning system
title_fullStr Morphological characteristics of retinal vessels in eyes with high myopia: Ultra-wide field images analyzed by artificial intelligence using a transfer learning system
title_full_unstemmed Morphological characteristics of retinal vessels in eyes with high myopia: Ultra-wide field images analyzed by artificial intelligence using a transfer learning system
title_short Morphological characteristics of retinal vessels in eyes with high myopia: Ultra-wide field images analyzed by artificial intelligence using a transfer learning system
title_sort morphological characteristics of retinal vessels in eyes with high myopia: ultra-wide field images analyzed by artificial intelligence using a transfer learning system
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982751/
https://www.ncbi.nlm.nih.gov/pubmed/36874950
http://dx.doi.org/10.3389/fmed.2022.956179
work_keys_str_mv AT maojianbo morphologicalcharacteristicsofretinalvesselsineyeswithhighmyopiaultrawidefieldimagesanalyzedbyartificialintelligenceusingatransferlearningsystem
AT dengxinyi morphologicalcharacteristicsofretinalvesselsineyeswithhighmyopiaultrawidefieldimagesanalyzedbyartificialintelligenceusingatransferlearningsystem
AT yeyu morphologicalcharacteristicsofretinalvesselsineyeswithhighmyopiaultrawidefieldimagesanalyzedbyartificialintelligenceusingatransferlearningsystem
AT liuhui morphologicalcharacteristicsofretinalvesselsineyeswithhighmyopiaultrawidefieldimagesanalyzedbyartificialintelligenceusingatransferlearningsystem
AT fangyuyan morphologicalcharacteristicsofretinalvesselsineyeswithhighmyopiaultrawidefieldimagesanalyzedbyartificialintelligenceusingatransferlearningsystem
AT zhangzhengxi morphologicalcharacteristicsofretinalvesselsineyeswithhighmyopiaultrawidefieldimagesanalyzedbyartificialintelligenceusingatransferlearningsystem
AT chennuo morphologicalcharacteristicsofretinalvesselsineyeswithhighmyopiaultrawidefieldimagesanalyzedbyartificialintelligenceusingatransferlearningsystem
AT sunmingzhai morphologicalcharacteristicsofretinalvesselsineyeswithhighmyopiaultrawidefieldimagesanalyzedbyartificialintelligenceusingatransferlearningsystem
AT shenlijun morphologicalcharacteristicsofretinalvesselsineyeswithhighmyopiaultrawidefieldimagesanalyzedbyartificialintelligenceusingatransferlearningsystem