Accurate and detailed segmentation

With CAC’s automatic segmentation of the heart and surrounding structures, CAC can accurately analyze the calcified plaques in coronary arteries.

Accurate calcification detection
in coronary arteries

ROBINSCA* clinical examination of 997 non-contrast ECG CT images Performance evaluation was performed through CAC detection and quantification.

*ROBINSCA : Risk Or Benefit IN Screening for CArdiovascular disease

• Medical AI diagnostic accuracy: 99.2%
• Detection and classification concordance: 87%
• Agatston score concordance: 95%

Diagnoses are also available
from chest CTs

Coronary artery calcification can be quantified not only on heart CT images but also on chest CT images, helping early detection, and reducing patient exposure.

• Kernel conversion AI technology is applied.

Predicting risk
with the lastest calssification method

Using CAC-DRS* is better for predicting risk than just using Agatston scores on non-contrast and non-cardiac CT scans.

*CAC-DRS: Coronary Artery Calcium Data and Reporting System.
An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT)

• Represents the total calcium score and the number of involved arteries.
• General recommendations are provided for further management

Integration in all standard reading environments

Integration in all solutions comply with DICOM, TCP/IP
(PACS & 3rd party solutions)

• DICOM
• HL7
• Shared folder and Open API
• Outputs: Annotated images, DICOM SR, pdf
• Deployment:
  Locally on dedicated hardware, Locally virtualized (docker), Cloud-based

Entirely automated process

Pre-analysis is possible with fully automatic work procedures, and reports containing accurate and detailed results can be used in clinical practice.