M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images

• Published in: Network (Bristol) Vol. 35; no. 3; p. 319
• Main Authors: Gurumurthy, Premalatha, Alagarsamy, Manjunathan, Kuppusamy, Sangeetha, Ponnusamy, Niranjana Chitra
• Format: Journal Article
• Language: English
• Published: England 01.08.2024
• Subjects: Algorithms; Artificial Intelligence; Cardiovascular Diseases - diagnostic imaging; Fundus Oculi; Humans; Neural Networks, Computer; Qatar; Risk Assessment - methods; Qatar; deep learning; Multi-modal features; multi-modal artificial intelligence; convolutional neural network; segmentation; cardiovascular diseases; fundus images
• ISSN: 1361-6536
• DOI: 10.1080/0954898X.2024.2306988

Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD. The M2AI-CVD framework employs a four-fold methodology: First, it rigorously evaluates image quality and processes lower-quality images for further analysis. Subsequently, it uses the Entropy-based Fuzzy C Means (EnFCM) algorithm for precise image segmentation. The Multi-Modal Boltzmann Machine (MMBM) is then employed to extract relevant features from various data modalities, while the Genetic Algorithm (GA) selects the most informative features. Finally, a ZFNet Convolutional Neural Network (ZFNetCNN) classifies images, effectively distinguishing between CVD and Non-CVD cases. The research's culmination, tested across five distinct datasets, yields outstanding results, with an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%. This multi-modal AI approach offers a promising solution for the accurate and early detection of cardiovascular diseases, significantly improving the prospects of timely intervention and improved patient outcomes in the realm of cardiovascular health.