Coronary artery calcification quantification

Principal Investigator

Name
Joris Wakkie

Degrees
MD

Institution
Aidence

Position Title
Chief Medical Officer

Email
joris@aidence.com

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.

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