Welcome to Noval Tech’s Cardiovascular Projects, where we utilize advanced technologies to improve cardiac health diagnosis. The first project focuses on automating the analysis of MRI scans through deep learning-based segmentation. This approach identifies and measures the volume and morphology of both the left and right ventricles, providing valuable data for assessing heart disease.
The second project applies state-of-the-art deep learning techniques and volumetric heart segmentation to deliver precise evaluations of two key aspects of heart health: Coronary Artery Disease (CAD) and ventricular function.
Project 1
Project Name: Deep Learning Based Volumetric Segmentation of Heart
Ventricles for Assessment of Cardiac Disease Using MRI
Diagnosis of cardiovascular diseases through cardiac MRI imaging plays a crucial role. Manual evaluation is time consuming and prone to errors. With the help of deep learning, a lot of traction has been developed for cardiac imaging diagnosis. In this study, we present a fully automated pipeline for the segmentation of left ventricle, right ventricle, myocardium, and classification of cardiovascular diseases into five classes using the cardiac MRI scans from the ACDC dataset. We adopted Segnet architecture for segmentation and made a comparative analysis using 2D and 3D approach. Best results were obtained using 2D approach with dice scores of 0.877(RV), 0.877(MYO), 0.937(LV) on the test set.We later on use the segmentation outputs to extract quantitative features to develop a robust classifier that gave us an overall accuracy of 85% on the test set and 0.81,0.89 scores of precisions and recall. Our proposed approach is computationally efficient and can be used for making critical decisions during diagnosis.
Project 2
Title: Diagnosis of Coronary Artery Disease using Adult Data from Blood Tests and Electrocardiograms
Abstract: Many modifiable risk factors affect the onset of coronary artery disease (CAD), a condition that is extremely common throughout the globe. Predictive models created using machine learning (ML) algorithms may help physicians identify CAD earlier and may lead to better results. The goal of this project was to use ML algorithms to predict CAD in patients.
