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      Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images

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          Abstract

          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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document} 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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document} 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.

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          Most cited references24

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          Artificial intelligence and deep learning in ophthalmology

          Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.
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            Five-Year Outcomes of Panretinal Photocoagulation vs Intravitreous Ranibizumab for Proliferative Diabetic Retinopathy

            Ranibizumab is a viable treatment option for eyes with proliferative diabetic retinopathy (PDR) through 2 years. However, longer-term results are needed.
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              The relationship of glycemic exposure (HbA1c) to the risk of development and progression of retinopathy in the diabetes control and complications trial.

              The Diabetes Control and Complications Trial (DCCT) demonstrated that a regimen of intensive therapy aimed at maintaining near-normal blood glucose values markedly reduces the risks of development or progression of retinopathy and other complications of insulin-dependent diabetes mellitus (IDDM) when compared with a conventional treatment regimen. This report presents an epidemiological assessment of the association between levels of glycemic exposure (HbA1c) before and during the DCCT with the risk of retinopathy progression within each treatment group. The initial level of HbA1c observed at eligibility screening as an index of pre-DCCT glycemia and the duration of IDDM on entry were the dominant baseline predictors of the risk of progression. The shorter the duration of IDDM on entry, the greater were the benefits of intensive therapy. In each treatment group, the mean HbA1c during the trial was the dominant predictor of retinopathy progression, and the risk gradients were similar in the two groups; a 10% lower HbA1c (e.g., 8 vs. 7.2%) is associated with a 43% lower risk in the intensive group and a 45% lower risk in the conventional group. These risk gradients applied over the observed range of HbA1c values and were unaffected by adjustment for other covariates. Over the range of HbA1c achieved by DCCT intensive therapy, there does not appear to be a level of glycemia below which the risks of retinopathy progression are eliminated. The change in risk over time, however, differed significantly between the treatment groups, the risk increasing with time in the study in the conventional group but remaining relatively constant in the intensive group. The risks were compounded by a multiplicative effect of the level of HbA1c with the duration of exposure (time in study). Total glycemic exposure was the dominant factor associated with the risk of retinopathy progression. When examined simultaneously within each treatment group, each of the components of pre-DCCT glycemic exposure (screening HbA1c value and IDDM duration) and glycemic exposure during the DCCT (mean HbA1c, time in study, and their interaction) were significantly associated with risk of retinopathy progression. Similar results also apply to other retinopathic, nephropathic, and neuropathic outcomes. The recommendation of the DCCT remains that intensive therapy with the goal of achieving near-normal glycemia should be implemented as early as possible in as many IDDM patients as is safely possible.
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                Author and article information

                Contributors
                yexinsarah@163.com
                tongyoung9123@163.com
                hscflea@163.com
                zxx15888859591@163.com
                Josie214@163.com
                wangyaqi@cuz.edu.cn
                shaohang@tsinghua.edu.cn
                slj@mail.eye.ac.cn
                Journal
                Ophthalmol Ther
                Ophthalmol Ther
                Ophthalmology and Therapy
                Springer Healthcare (Cheshire )
                2193-8245
                2193-6528
                15 June 2023
                15 June 2023
                October 2023
                : 12
                : 5
                : 2441-2452
                Affiliations
                [1 ]GRID grid.417401.7, ISNI 0000 0004 1798 6507, Department of Ophthalmology, Center for Rehabilitation Medicine, , Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College), ; Hangzhou, Zhejiang China
                [2 ]GRID grid.268099.c, ISNI 0000 0001 0348 3990, Wenzhou Medical University, ; Wenzhou, Zhejiang China
                [3 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Jiaxing Key Laboratory of Visual Big Data and Artificial Intelligence, , Yangtze Delta Region Institute of Tsinghua University, ; Zhejiang, China
                [4 ]GRID grid.449896.e, ISNI 0000 0004 1755 0017, College of Media Engineering, , Communication University of Zhejiang, ; Hangzhou, China
                Author information
                http://orcid.org/0000-0002-7518-2932
                Article
                746
                10.1007/s40123-023-00746-5
                10441848
                37318706
                1ece542e-c394-4637-a910-70db2bc47c6b
                © The Author(s) 2023

                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/.

                History
                : 6 April 2023
                : 25 May 2023
                Funding
                Funded by: Zhejiang Medical and Health Science and Technology Plan Project
                Award ID: 2023KY915
                Award Recipient :
                Categories
                Original Research
                Custom metadata
                © Springer Healthcare Ltd., part of Springer Nature 2023

                anti-vascular endothelial growth factor,artificial intelligence,central macular fluid volume,central subfield thickness,diabetic macular edema,optical coherence tomography,predictive preventive personalized medicine

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