HyMNet: A Multimodal Deep Learning System for Hypertension Prediction Using Fundus Images and Cardiometabolic Risk Factors

HyMNet: A Multimodal Deep Learning System for Hypertension Prediction Using Fundus Images and Cardiometabolic Risk Factors

Study Objectives: This study aimed to develop a multimodal deep learning (MMDL) system called HyMNet, integrating fundus images and cardiometabolic factors (age and sex) to enhance hypertension (HTN) detection. Methods: HyMNet employed RETFound, a model pretrained on 1.6 million retinal images, for...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohammed Baharoon, Hessa Almatar, Reema Alduhayan, Tariq Aldebasi, Badr Alahmadi, Yahya Bokhari, Mohammed Alawad, Ahmed Almazroa, Abdulrhman Aljouie
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Bioengineering
Subjects:
artificial intelligence
machine learning
computer vision
cardiovascular diseases
hypertension detection
fundus images
Online Access:https://www.mdpi.com/2306-5354/11/11/1080
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items