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...
Autores principales: | , , , , , , , , |
---|---|
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 |