Coronary artery calcification quantification
Principal Investigator
Name
Joris Wakkie
Degrees
MD
Institution
Aidence
Position Title
Chief Medical Officer
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-514
Initial CDAS Request Approval
May 28, 2019
Title
Coronary artery calcification quantification
Summary
The presence of CVD can be detected in CT scans by measuring the amount of Coronary Artery
Calcification (CAC), a strong and independent predictor of cardiovascular events. Automatic calcium scoring could be a viable method that would enable routine cardiovascular risk prediction in various clinical settings.
Our goal is to develop and validate a Deep Learning software product, intended for the detection and quantification of coronary artery calcification on both non-gated CT chest and cardiac CT scans with the aim to assist physicians in the risk assessment of cardiovascular disease.
Calcification (CAC), a strong and independent predictor of cardiovascular events. Automatic calcium scoring could be a viable method that would enable routine cardiovascular risk prediction in various clinical settings.
Our goal is to develop and validate a Deep Learning software product, intended for the detection and quantification of coronary artery calcification on both non-gated CT chest and cardiac CT scans with the aim to assist physicians in the risk assessment of cardiovascular disease.
Aims
1. To develop Deep Learning models for CAC detection and quantification that are outperforming the current state-of-the-art, as described in the literature;
2. To develop and validate a software product for the detection and quantification of coronary artery calcification on both non-gated CT chest scans and cardiac CT scan.
Collaborators
Gerben van Veenendaal
Mark-Jan Harte