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

HyMNet: a Multimodal Deep Learning System for Hypertension Classification using Fundus Photographs and Cardiometabolic Risk Factors