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Automated evaluation of retinal hyperreflective foci changes in diabetic macular edema patients before and after intravitreal injection

Authors :
Xingguo Wang
Yanyan Zhang
Yuhui Ma
William Robert Kwapong
Jianing Ying
Jiayi Lu
Shaodong Ma
Qifeng Yan
Quanyong Yi
Yitian Zhao
Source :
Frontiers in Medicine, Vol 10 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

PurposeFast and automated reconstruction of retinal hyperreflective foci (HRF) is of great importance for many eye-related disease understanding. In this paper, we introduced a new automated framework, driven by recent advances in deep learning to automatically extract 12 three-dimensional parameters from the segmented hyperreflective foci in optical coherence tomography (OCT).MethodsUnlike traditional convolutional neural networks, which struggle with long-range feature correlations, we introduce a spatial and channel attention module within the bottleneck layer, integrated into the nnU-Net architecture. Spatial Attention Block aggregates features across spatial locations to capture related features, while Channel Attention Block heightens channel feature contrasts. The proposed model was trained and tested on 162 retinal OCT volumes of patients with diabetic macular edema (DME), yielding robust segmentation outcomes. We further investigate HRF’s potential as a biomarker of DME.ResultsResults unveil notable discrepancies in the amount and volume of HRF subtypes. In the whole retinal layer (WR), the mean distance from HRF to the retinal pigmented epithelium was significantly reduced after treatment. In WR, the improvement in central macular thickness resulting from intravitreal injection treatment was positively correlated with the mean distance from HRF subtypes to the fovea.ConclusionOur study demonstrates the applicability of OCT for automated quantification of retinal HRF in DME patients, offering an objective, quantitative approach for clinical and research applications.

Details

Language :
English
ISSN :
2296858X
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.1325de4b4833a206f61c3014c03a
Document Type :
article
Full Text :
https://doi.org/10.3389/fmed.2023.1280714