Using deep learning to screen OCTA images for hypertension to reduce the risk of serious complications

TL;DR

This study uses deep learning on OCTA images to detect hypertension, aiming to improve screening and reduce complications.

Contribution

The novel use of Xception and Swin transformer models to analyze OCTA images for hypertension detection.

Findings

Xception achieved 76.05% accuracy in 5-fold cross-validation for hypertension screening.

Swin transformer multimodel achieved 85.06% average accuracy in predicting hypertension.

Deep learning models can efficiently detect hypertension-related changes in ocular microvessels.

Abstract

As a disease with high global incidence, hypertension is known to cause systemic vasculopathy. Ophthalmic vessels are the only vascular structures that can be directly observed in vivo in a non-invasive manner. We aim to investigate the changes in ocular microvessels in hypertension using deep learning on optical coherence tomography angiography (OCTA) images.

The convolutional neural network architecture Xception and multi-Swin transformer were used to screen 422 OCTA images (252 from 136 hypertension subjects; 170 from 85 healthy subjects) for hypertension. Moreover, the separability of the OCTA images based on high-dimensional feature angles was analyzed to better understand how deep learning models distinguish such images with class activation mapping.

Under Xception, the overall average accuracy of 5-fold cross-validation was 76.05% and sensitivity was 85.52%. In contrast, the Swin transformer showed single-model (macular), single-model (optic disk), and multimodel average accuracies of 82.25%, 74.936%, and 85.06%, respectively, for predicting hypertension.

The changes caused by hypertension on the fundus vessels can be observed more accurately and efficiently using OCTA image features through deep learning. These results are expected to assist with screening of hypertension and reducing the risk of its severe complications.

ChiCTR, ChiCTR2000041330. Registered 23 December 2020, https://www.chictr.org.cn/ChiCTR2000041330.

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