In recent years, deep learning has shown promise in predicting hypertension
(HTN) from fundus images. However, most prior research has primarily focused on
analyzing a single type of data, which may not capture the full complexity of
HTN risk. To address this limitation, this study introduces a multimodal deep
learning (MMDL) system, dubbed HyMNet, which combines fundus images and
cardiometabolic risk factors, specifically age and gender, to improve
hypertension detection capabilities. Our MMDL system uses the DenseNet-201
architecture, pre-trained on ImageNet, for the fundus imaging path and a fully
connected neural network for the age and gender path. The two paths are jointly
trained by concatenating 64 features output from each path that are then fed
into a fusion network. The system was trained on 1,143 retinal images from 626
individuals collected from the Saudi Ministry of National Guard Health Affairs.
The results show that the multimodal model that integrates fundus images along
with age and gender achieved an AUC of 0.791 [CI: 0.735, 0.848], which
outperforms the unimodal model trained solely on fundus photographs that
yielded an AUC of 0.766 [CI: 0.705, 0.828] for hypertension detection.

通过融合眼底图像、年龄和性别，采用深度学习技术开发了一种多模态深度学习系统 HyMNet，用于提高高血压检测的准确性。该系统结合了眼底图像和心血管代谢风险因素，通过 DenseNet-201 架构和全连接神经网络进行联合训练，并取得了相比单模态模型更高的高血压检测 AUC 0.791。