AVIEW CAC by Coreline
AVIEW CAC supports the diagnosis of coronary arteriosclerosis by supporting analysis of cardiac CT scans.1
- Coronary Arteriosclerosis
- Cardiac
- Cardiovascular
- CAC
- CT
- Diagnostic
- Reporting
The Global Burden of CVD
Cardiovascular diseases (CVDs) are the leading cause of mortality globally.2 Coronary artery calcium (CAC) has a strong association with CVD.2 CAC scoring has a high predictive value for incidental coronary events in asymptomatic individuals and is an effective tool in early detection and risk stratification of CVD.2
Automatically Analyzes the Qualitative Figure of Coronary Artery Lithography1
AVIEW CAC is software that uses artificial intelligence technology to automatically split the cardiovascular system from heart CT images and automatically analyzes the quantitative figures of coronary artery lithography to assist in the diagnosis of coronary arteriosclerosis due to calcified coronary lesion.1
Quantitatively analyze coronary artery calcification with AI technology.1
Efficiency. Reliability. Patient Care.
- Automatic Report Generation1
- Mean analysis in less than 1 minute3*
- Easy to manage data1**
- 99.2% AI diagnostic accuracy4***
- 87% Detection & classification concordance in cardiac screening4
- 95% Agatston score concordance in cardiac screening4
- CAC score is provided for each blood vessel and risk distribution by age group according to clinical criteria and blood vessel age are provided to help patient understanding1
- High reliability & accuracy for assignment of risk classification3
- Produces Agatston scores for both ECG-gated (CSCT) and non-ECG gated (LDCT)3,4
* In non-ECG gated LDCT, indicating potential for use in clinical practice
** Hospital PACS data can be easily exchanged with the DICOM transmission protocol.
*** AI accurately detects CAC at an expert level in cardiac screening
1 AVIEW User Guide
2 Denissen SJAM, van der Aalst CM, Vonder M, et al. Screening for coronary artery calcium in a high-risk population: the ROBINSCA trial. Eur J Prev Cardiol. 2021;28(10):1155-1159. doi:10.1177/2047487320932263
3 Kang HW, Ahn WJ, Jeong JH, et al. Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness. Eur Radiol. 2023;33(3):1973-1981. doi:10.1007/s00330-022-09143-1
4 Vonder M, Zheng S, Dorrius MD, et al. Deep Learning for Automatic Calcium Scoring in Population-Based Cardiovascular Screening. JACC Cardiovasc Imaging. 2022;15(2):366-367. doi:10.1016/j.jcmg.2021.07.012
Currently not available within the Calantic Viewer.
