M2AI-CVD: Multi-modal AI approach cardiovascular risk prediction system using fundus images
Premalatha Gurumurthy1, Manjunathan Alagarsamy2, Sangeetha Kuppusamy3
1Department of Electronics and Communication Engineering, Prathyusha Engineering College, Tiruvallur, Tamil Nadu, India.
Insights
This study introduces an AI system for early cardiovascular disease (CVD) detection using non-invasive methods. The Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD) system achieved high accuracy in identifying CVD cases.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Cardiovascular diseases (CVD) are a leading global health concern, often diagnosed late.
- Non-invasive CVD detection methods, like retinal imaging, are under-researched in regions such as Qatar.
- Early detection is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate a novel Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD) system.
- To enhance the accuracy and efficiency of non-invasive CVD identification.
- To address the limited research on advanced CVD detection techniques in specific geographical areas.
Main Methods:
- Image quality assessment and processing for lower-quality inputs.
- Entropy-based Fuzzy C Means (EnFCM) for precise image segmentation.
- Multi-Modal Boltzmann Machine (MMBM) for feature extraction and Genetic Algorithm (GA) for feature selection.
- ZFNet Convolutional Neural Network (ZFNetCNN) for classifying CVD and Non-CVD cases.
Main Results:
- The M2AI-CVD system demonstrated high performance across five datasets.
- Achieved an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%.
- The multi-modal AI approach proved effective in distinguishing between CVD and Non-CVD cases.
Conclusions:
- The M2AI-CVD system offers a promising, accurate solution for early cardiovascular disease detection.
- This multi-modal AI approach can significantly improve patient outcomes through timely interventions.
- Highlights the potential of AI in advancing non-invasive diagnostic tools for cardiovascular health.
Abstract:
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.
More Related Videos
07:51Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
Published on: September 26, 2018
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
