Predicting cerebral small vessel disease through retinal scans and demographic data with Bayesian feature selection

Changkai Ji; Jing Li; Changde Du; Bin Lv; Ning Wu; Hongyang Li; Rui Li; Ying Hui; Guotong Xie; Shoulin Wu; Zhenchang Wang; Huiguang He; Dinggang Shen

doi:10.1117/12.3006453

3 April 2024 Predicting cerebral small vessel disease through retinal scans and demographic data with Bayesian feature selection

Changkai Ji, Jing Li, Changde Du, Bin Lv, Ning Wu, Hongyang Li, Rui Li, Ying Hui, Guotong Xie, Shoulin Wu, Zhenchang Wang, Huiguang He, Dinggang Shen

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129273G (2024) https://doi.org/10.1117/12.3006453
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Ophthalmic examinations, including fundus and optical coherence tomography (OCT) imaging, are integral in diagnosing systemic diseases affecting the eye, such as diabetic retinopathy. Recent studies have highlighted the related pathological features between retinal and cerebral small vessels, suggesting that retinal microvascular changes could reflect the status of cerebral small vessel disease (CSVD). In this research, we assessed patient’s fundus and OCT images, with a focus on CSVD severity. By incorporating patient demographic data, we developed a sparse Bayesian-based model using retinal and fundus vascular imaging for CSVD diagnosis, achieving an accuracy (ACC) of 71.71% and an area under the curve (AUC) of 73.35%. Our findings indicate that ophthalmic examinations can be a cost-effective screening method for CSVD. Implementing this methodology in optician and eye clinics may substantially lessen societal and familial impacts relative to conventional magnetic resonance imaging (MRI) diagnostics. Additionally, this study identified crucial early warning signs of severe CSVD, such as global venous width, and provided valuable insights into the nature of the disease.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Changkai Ji, Jing Li, Changde Du, Bin Lv, Ning Wu, Hongyang Li, Rui Li, Ying Hui, Guotong Xie, Shoulin Wu, Zhenchang Wang, Huiguang He, and Dinggang Shen "Predicting cerebral small vessel disease through retinal scans and demographic data with Bayesian feature selection", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129273G (3 April 2024); https://doi.org/10.1117/12.3006453

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