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. 2024 Jul 1;65(8):50.
doi: 10.1167/iovs.65.8.50.

Association of Retinal Biomarkers With the Subtypes of Ischemic Stroke and an Automated Classification Model

Zhouwei Xiong  1   2 , William R Kwapong  3 , Shouyue Liu  1   2 , Tao Chen  1   2 , Keyi Xu  1   2 , Haiting Mao  1   2 , Jinkui Hao  1 , Le Cao  3 , Jiang Liu  4 , Yalin Zheng  5 , Hang Wang  3 , Yuying Yan  3 , Chen Ye  3 , Bo Wu  3 , Hong Qi  6 , Yitian Zhao  1   2
Affiliations

Association of Retinal Biomarkers With the Subtypes of Ischemic Stroke and an Automated Classification Model

Zhouwei Xiong et al. Invest Ophthalmol Vis Sci. .

Abstract

Purpose: Retinal microvascular changes are associated with ischemic stroke, and optical coherence tomography angiography (OCTA) is a potential tool to reveal the retinal microvasculature. We investigated the feasibility of using the OCTA image to automatically identify ischemic stroke and its subtypes (i.e. lacunar and non-lacunar stroke), and exploited the association of retinal biomarkers with the subtypes of ischemic stroke.

Methods: Two cohorts were included in this study and a total of 1730 eyes from 865 participants were studied. A deep learning model was developed to discriminate the subjects with ischemic stroke from healthy controls and to distinguish the subtypes of ischemic stroke. We also extracted geometric parameters of the retinal microvasculature at different retinal layers to investigate the correlations.

Results: Superficial vascular plexus (SVP) yielded the highest areas under the receiver operating characteristic curve (AUCs) of 0.922 and 0.871 for the ischemic stroke detection and stroke subtypes classification, respectively. For external data validation, our model achieved an AUC of 0.822 and 0.766 for the ischemic stroke detection and stroke subtypes classification, respectively. When parameterizing the OCTA images, we showed individuals with ischemic strokes had increased SVP tortuosity (B = 0.085, 95% confidence interval [CI] = 0.005-0.166, P = 0.038) and reduced FAZ circularity (B = -0.212, 95% CI = -0.42 to -0.005, P = 0.045); non-lacunar stroke had reduced SVP FAZ circularity (P = 0.027) compared to lacunar stroke.

Conclusions: Our study demonstrates the applicability of artificial intelligence (AI)-enhanced OCTA image analysis for ischemic stroke detection and its subtypes classification. Biomarkers from retinal OCTA images can provide useful information for clinical decision-making and diagnosis of ischemic stroke and its subtypes.

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Conflict of interest statement

Disclosure: Z. Xiong, None; W.R. Kwapong, None; S. Liu, None; T. Chen, None; K. Xu, None; H. Mao, None; J. Hao, None; L. Cao, None; J. Liu, None; Y. Zheng, None; H. Wang, None; Y. Yan, None; C. Ye, None; B. Wu, None; H. Qi, None; Y. Zhao, None

Figures

Figure 1.
Figure 1.
The definitions of OCTA stratification. In this study, SVP, ICP, and DCP en face images are included.
Figure 2.
Figure 2.
The workflow of our ASI–Net for stroke detection. The backbone was used to extract feature maps from the inputted OCTA en face images. The feature maps were also provided to the memory bank and used the contrastive learning method to learn the differences between the two different groups. Last, these features were sent to the classifier to output the prediction result.
Figure 3.
Figure 3.
Illustration results of vascular parameters. (a) This shows a 3 × 3 mm2 en face SVP angiogram. (b) This is the detected FAZ area (FA), and (c) shows its perimeter (FP). The FAZ circularity is calculated as: FC = 4π · FA/FP 2. (d) Illustrates the vascular branch map, it was used to segment vascular and calculate tortuosity.
Figure 4.
Figure 4.
Flowchart for excluding participants who fail to meet the specified requirements. A total of 1730 eyes from 865 subjects were included in the study. IS denotes the ischemic stroke. I and T denotes the internal dataset and independent test dataset, respectively.
Figure 5.
Figure 5.
The heatmaps of the proposed model. (a) Presents the heatmaps of three randomly –selected healthy control (A–D) and three patients with stroke (D–F). (b) Presents the heatmaps of three randomly – selected patients with non–lacunar stroke (G–I) and three patients with lacunar stroke (J–L).
See this image and copyright information in PMC

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