CAC Software by Imbio, Inc. is a machine learning-based post-processing tool that analyzes non-contrast CT images of the chest to assess calcified plaques in the coronary arteries. It quantifies the calcification burden by producing scores and metrics that help clinicians evaluate coronary artery disease risk. The software outputs annotated images and a detailed report, integrating into the standard DICOM workflow to assist physicians without making standalone diagnoses.
Imbio CAC Software is intended for use as a non-invasive post-processing software to evaluate calcified plaques in the coronary arteries, which present a risk for coronary artery disease. It uses machine learning to analyze non-contrast thoracic CT images and outputs a summary report including Agatston score, arterial age, and calcified lesion mass and volume metrics at whole heart and individual coronary artery levels. It also outputs annotated images for informational purposes only. Limited to adult patients 29 years and older. Does not diagnose coronary artery disease. Outputs integrate into standard DICOM viewing workflows. Results are not for stand-alone clinical decision-making or to replace clinical assessment of CT images.
The software is a set of medical image post-processing algorithms that perform automated coronary artery calcification segmentation and generate quantitative metrics such as Agatston score, calcified lesion mass, volume, and arterial age. It processes non-contrast thoracic CT DICOM datasets, outputs annotated DICOM images with color-coded segmentation overlays, and summary PDF reports. The software operates as a command-line executable and integrates into PACS viewing environments. It is not directly interfaced with CT scanners and is designed to support physicians' image analysis using machine learning.
A retrospective multi-center standalone device performance assessment was conducted on 500 anonymized chest CT series annotated by experienced technologists using FDA-cleared semi-automated CAC scoring software as ground truth. The dataset included both ECG-gated and non-gated CT scans from various vendors. The primary endpoint measured agreement of cardiovascular disease risk category using a 5-category scale, achieving a Cohen’s kappa of 0.907 (95% CI 0.895-0.920), exceeding the acceptance criterion of 0.859, demonstrating strong performance accuracy.
No predicate devices specified
Submission
1/17/2023
FDA Approval
6/13/2023
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