QURAI

AREA RESEARCH

Category: Cardiovascular

Research

Projects

    2023

     | 

    2027

    Predicting cardiovascular events and risk of disease progression for peripheral arterial disease

    Developing AI-based methods for multi-modal patient-specific prediction of PAD progression

    ONGOING

    2023

     | 

    2028

    AI for the analysis of cardiovascular risk in breast cancer survivors

    Developing AI tools for personalized prediction of cardiovascular disease and body composition quantification in breast cancer survivors undergoing radiotherapy treatment planning CT

    ONGOING

    2023

     | 

    2027

    Coronary artery analysis in high-end CCTA

    Developing robust deep learning models for non-invasive coronary artery stenosis assessment using spectral and photon-counting CCTAs

    ONGOING

    2023

     | 

    2027

    AI for real time coronary optical coherence tomography analysis

    Towards robust and trustworthy AI-systems for real-time analysis of coronary optical coherence tomography

    ONGOING

    2020

     | 

    2023

    AI for risk stratification and decision making in patients with coronary heart disease

    Analyzing cardiology data for improvement of the patient care

    FINISHED

    2021

     | 

    2025

    Risk stratification in coronary heart disease with echocardiography

    Developing AI-based risk stratification in coronary heart disease with echocardiography

    ONGOING

    2011

     | 

    2013

    Atherosclerotic calcifications of head and neck arteries

    Relating calcifications and stenosis in the internal carotid artery in patients with symptoms of cerebrovascular disease

    FINISHED

    2014

     | 

    2018

    Automatic detection of CVD and osteoporosis in lung cancer screening trials

    Developing AI methods for automatic detection of cardiovascular and spine diseases in chest CT

    FINISHED

    2012

     | 

    2017

    Cardiovascular phenotype-genotype analysis within a CT based lung cancer screening trial

    Developing machine learning methods to measure existing and novel imaging biomarkers related to cardiovascular disease in chest CT

    FINISHED

    2015

     | 

    2019

    Automatic identification of patients with functionally significant obstructive coronary artery disease using non-invasive cardiac CT

    Developing AI models to identify patients with functionally significant coronary artery disease.

    FINISHED

    2020

     | 

    2022

    Early detection of individuals at risk of cardiac events

    Developing deep learning techniques for accurate selection of patients at risk of acute coronary syndrome or sudden cardiac death from CT scans.

    FINISHED

    2016

     | 

    2020

    Cardiovascular risk in breast cancer patients with radiotherapy planning CT

    Automated cardiovascular risk prediction in breast cancer patients undergoing radiotherapy treatment planning CT

    FINISHED

    2020

     | 

    2024

    Non-invasive and intravascular identification and drug-eluting balloon treatment of vulnerable lipid-rich plaques

    Developing non-invasive AI-based method that detects and characterizes lipid-rich plaque in computed tomography coronary angiography.

    ONGOING

    2020

     | 

    2023

    Cardiac arrhythmia in patients with congenital heart disease

    Developing an algorithm that analyzes single-lead ECG to identify cardiac arrhythmia.

    FINISHED

    2017

     | 

    2022

    DLMedIA: Deep transfer learning in cardiovascular disease

    Developing deep transfer learning techniques to effectively analyze medical imaging data with variation in scanners, scan protocols, and patient populations.

    FINISHED

    2017

     | 

    2022

    DLMedIA: Deep generative models for cardiovascular disease

    Developing deep generative models to learn more efficiently from less data in the analysis of cardiac CT.

    FINISHED

    2017

     | 

    2022

    DLMedIA: High dimensional data for cardiovascular disease

    Developing deep learning techniques for quantitative analysis of 4- and 5-dimensional CT and MR images of the heart

    FINISHED

    2019

     | 

    2023

    AI for CCTA-based prediction of FFR

    Developing AI models to predict invasively measured fractional flow reserve noninvasively

    FINISHED