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Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images
INTRODUCTION: We studied the correlation of central macular fluid volume (CMFV) and central subfield thickness (CST) with best-corrected visual acuity (BCVA) in treatment-naïve eyes with diabetic macular edema (DME) 1 month after anti-vascular endothelial growth factor (VEGF) therapy. METHODS: This...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Healthcare
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10441848/ https://www.ncbi.nlm.nih.gov/pubmed/37318706 http://dx.doi.org/10.1007/s40123-023-00746-5 |
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author | Ye, Xin Gao, Kun He, Shucheng Zhong, Xiaxing Shen, Yingjiao Wang, Yaqi Shao, Hang Shen, Lijun |
author_facet | Ye, Xin Gao, Kun He, Shucheng Zhong, Xiaxing Shen, Yingjiao Wang, Yaqi Shao, Hang Shen, Lijun |
author_sort | Ye, Xin |
collection | PubMed |
description | INTRODUCTION: We studied the correlation of central macular fluid volume (CMFV) and central subfield thickness (CST) with best-corrected visual acuity (BCVA) in treatment-naïve eyes with diabetic macular edema (DME) 1 month after anti-vascular endothelial growth factor (VEGF) therapy. METHODS: This retrospective cohort study investigated eyes that received anti-VEGF therapy. All participants underwent comprehensive examinations and optical coherence tomography (OCT) volume scans at baseline (M0) and 1 month after the first treatment (M1). Two deep learning models were separately developed to automatically measure the CMFV and the CST. Correlations were analyzed between the CMFV and the logMAR BCVA at M0 and logMAR BCVA at M1. The area under the receiver operating characteristic curve (AUROC) of CMFV and CST for predicting eyes with BCVA [Formula: see text] 20/40 at M1 was analyzed. RESULTS: This study included 156 DME eyes from 89 patients. The median CMFV decreased from 0.272 (0.061–0.568) at M0 to 0.096 (0.018–0.307) mm(3) at M1. The CST decreased from 414 (293–575) to 322 (252–430) μm. The logMAR BCVA decreased from 0.523 (0.301–0.817) to 0.398 (0.222–0.699). Multivariate analysis demonstrated that the CMFV was the only significant factor for logMAR BCVA at both M0 (β = 0.199, p = 0.047) and M1 (β = 0.279, p = 0.004). The AUROC of CMFV for predicting eyes with BCVA [Formula: see text] 20/40 at M1 was 0.72, and the AUROC of CST was 0.69. CONCLUSIONS: Anti-VEGF therapy is an effective treatment for DME. Automated measured CMFV is a more accurate prognostic factor than CST for the initial anti-VEGF treatment outcome of DME. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40123-023-00746-5. |
format | Online Article Text |
id | pubmed-10441848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Healthcare |
record_format | MEDLINE/PubMed |
spelling | pubmed-104418482023-08-22 Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images Ye, Xin Gao, Kun He, Shucheng Zhong, Xiaxing Shen, Yingjiao Wang, Yaqi Shao, Hang Shen, Lijun Ophthalmol Ther Original Research INTRODUCTION: We studied the correlation of central macular fluid volume (CMFV) and central subfield thickness (CST) with best-corrected visual acuity (BCVA) in treatment-naïve eyes with diabetic macular edema (DME) 1 month after anti-vascular endothelial growth factor (VEGF) therapy. METHODS: This retrospective cohort study investigated eyes that received anti-VEGF therapy. All participants underwent comprehensive examinations and optical coherence tomography (OCT) volume scans at baseline (M0) and 1 month after the first treatment (M1). Two deep learning models were separately developed to automatically measure the CMFV and the CST. Correlations were analyzed between the CMFV and the logMAR BCVA at M0 and logMAR BCVA at M1. The area under the receiver operating characteristic curve (AUROC) of CMFV and CST for predicting eyes with BCVA [Formula: see text] 20/40 at M1 was analyzed. RESULTS: This study included 156 DME eyes from 89 patients. The median CMFV decreased from 0.272 (0.061–0.568) at M0 to 0.096 (0.018–0.307) mm(3) at M1. The CST decreased from 414 (293–575) to 322 (252–430) μm. The logMAR BCVA decreased from 0.523 (0.301–0.817) to 0.398 (0.222–0.699). Multivariate analysis demonstrated that the CMFV was the only significant factor for logMAR BCVA at both M0 (β = 0.199, p = 0.047) and M1 (β = 0.279, p = 0.004). The AUROC of CMFV for predicting eyes with BCVA [Formula: see text] 20/40 at M1 was 0.72, and the AUROC of CST was 0.69. CONCLUSIONS: Anti-VEGF therapy is an effective treatment for DME. Automated measured CMFV is a more accurate prognostic factor than CST for the initial anti-VEGF treatment outcome of DME. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40123-023-00746-5. Springer Healthcare 2023-06-15 2023-10 /pmc/articles/PMC10441848/ /pubmed/37318706 http://dx.doi.org/10.1007/s40123-023-00746-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Original Research Ye, Xin Gao, Kun He, Shucheng Zhong, Xiaxing Shen, Yingjiao Wang, Yaqi Shao, Hang Shen, Lijun Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images |
title | Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images |
title_full | Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images |
title_fullStr | Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images |
title_full_unstemmed | Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images |
title_short | Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images |
title_sort | artificial intelligence-based quantification of central macular fluid volume and va prediction for diabetic macular edema using oct images |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10441848/ https://www.ncbi.nlm.nih.gov/pubmed/37318706 http://dx.doi.org/10.1007/s40123-023-00746-5 |
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