Autoplaque
by Cedars-Sinai Medical Center: AIM
· Based in United States →
— AI-Powered Quantitative Analysis for Coronary Plaque and Stenosis
Not disclosed
Regulatory Status Disclosed
Overview
Autoplaque is an AI-enabled, workstation-based post-processing software application developed by Cedars-Sinai Medical Center: AIM. It is designed to analyze coronary anatomy and pathology from Computed Tomography (CT) Angiographic images. The tool assists cardiologists and radiologists by providing an interactive platform for viewing and analyzing cardiac CT data, specifically to determine the presence and extent of coronary plaques and luminal stenoses. Autoplaque delivers automated, AI-powered quantitative analysis of atherosclerotic plaque, serving as a powerful tool for risk prediction and guiding treatment decisions. It is important to note that Autoplaque is intended to aid, not replace, the skill and judgment of a qualified medical practitioner.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered quantitative analysis of atherosclerotic plaque
- Detection and characterization of coronary artery plaque
- Measurement of luminal stenoses
- Analysis of coronary anatomy and pathology from CT Angiographic images
- Workstation-based post-processing application
- Automated vessel, plaque, and lumen segmentation (reviewable and editable by clinician)
- Review of heart and coronary vessels in Multiplanar Reformatting (MPR), curved MPR, and straightened MPR views
- Measurement of vessel diameter and area
- Characterization and measurement of plaque parameters
- Measurement of lumen diameter, area, and luminal stenosis
Use Cases
- Predicting heart attack risk
- Guiding treatment paths for coronary artery disease
- Non-invasive diagnostic reading of cardiac CT data
- Assessing artery health
- Quantifying plaque buildup in heart's arteries
- Characterizing dangerous plaque
What Physicians Need to Know
Cardiac CT/MRI Analysis
Autoplaque is an FDA-cleared software tool that analyzes cardiac CT angiography (CCTA) images to quantify both dangerous soft plaque and stable calcified plaque, as well as measure coronary artery stenosis. It performs post-processing of CCTA images, including automated vessel, plaque, and lumen segmentation, which can be reviewed and edited by the clinician. The tool can also glean valuable insights into heart health from other CT scans that include the entire chest, such as lung scans.
Heart Failure Risk Prediction
By accurately quantifying coronary plaque volume and composition, Autoplaque contributes to improved heart attack risk prediction, which is a significant factor in heart failure development. Research indicates that plaque volume is a stronger predictor of myocardial infarction (MI) than arterial stenosis. Patients in higher percentiles for plaque volume have been shown to be at increased risk of myocardial infarction.
Coronary Artery Assessment
Autoplaque provides standardized measurement and characterization of coronary plaque from CCTA, including total plaque, noncalcified plaque, low-attenuation plaque, and calcified plaque volumes. It accurately predicts heart attack risk within five years based on the amount and composition of plaque. The AI algorithm can quantify plaque from CTA images in 5-6 seconds, a significant reduction from the 25-30 minutes required by expert manual analysis.
AHA/ACC Guideline Alignment
While not explicitly stating direct alignment, Autoplaque's FDA clearance and extensive validation in over 100 published studies, including multicenter international trials like SCOT-HEART, suggest its methodology and clinical utility are consistent with established cardiovascular imaging and risk assessment guidelines.
Physician Tip
Leverage Autoplaque for rapid and objective quantification of coronary plaque and stenosis from CCTA images, enabling faster prognoses and enhanced preventative care. The tool's ability to identify high-risk plaque, even in patients with seemingly healthy vessel narrowing, represents a paradigm shift in risk stratification. Consider utilizing its insights from routine chest CT scans to uncover incidental cardiac health information. Remember that while the AI provides automated analysis, clinician review and editing of segmentations are still part of the workflow.
Autoplaque is a workstation-based post-processing application that works with DICOM image data. Cedars-Sinai has licensed Autoplaque technology to HeartFlow, Inc. for integration into their FFRct Analysis to provide plaque assessment in future products. APQ Health, a startup founded by the developers of Autoplaque, holds the exclusive worldwide license for the software.
Details
| Category | Cardiology AI, Radiology & Imaging AI |
| Pricing | Not disclosed — N/A |
| Deployment | Workstation-based software, runs on Windows or Mac computer platforms |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated Autoplaque 3.0 (K212758) is a Class II medical device (Product Code LLZ, Regulation 21 CFR §892.2050) cleared by the FDA. It is a stand-alone software that performs post-processing of coronary Computed Tomography Angiography (CTA) images and measurements of components of images using computerized algorithms. It aids physicians with the measurement of coronary artery stenosis and provides measurements for coronary plaque and coronary artery remodeling. |
| Integrations | |
| EHR | Not specified |
| Specialties | Cardiology, Radiology |
What the Web Says
Autoplaque, developed by Cedars-Sinai Medical Center, is a workstation-based, non-invasive diagnostic software designed for cardiologists and radiologists. It uses AI and deep learning to analyze cardiac CT data, aiding in the detection and measurement of coronary plaques and luminal stenoses. The software aims to optimize the analysis of coronary anatomy and pathology to help determine treatment paths.
Overall: PositiveStrengths
- Provides optimized non-invasive analysis of coronary anatomy and pathology.
- Aids in determining treatment paths from CT angiographic images.
- Utilizes deep learning for plaque volume measurements, which can enhance risk stratification.
- Faster analysis time compared to manual methods (less than 2 seconds per lesion for Autoplaque 3.0 vs. 30 seconds per lesion for predicate device, and 5.7 seconds per patient vs. 25-30 minutes for experts).
- Automated vessel, plaque, and lumen segmentation with clinician review and editing capabilities.
- Can run on Windows or Mac computer platforms.
- Reproducibility analysis yielded identical plaque and lumen measurements across repeated runs.
- All segmentations and plaque classifications were visually reviewed and accepted as anatomically correct by expert readers, requiring no manual adjustments in a study.
- AI-enabled plaque analysis can help physicians advise patients about heart attack risk.
Limitations
- Not intended to replace the skill and judgment of a qualified medical practitioner.
- Users must be appropriately trained in the software's functions, capabilities, and limitations.
- Certain views use interpolated data, which may give the appearance of healthy tissue where pathology exists.
- Requires installation on a suitable commercial computer platform, and users are responsible for monitor quality and ambient light conditions.
- Some deep learning-based algorithms for CCTA analysis can be sensitive to technical imaging parameters, potentially influencing output consistency.
Based on reviews from: FDA, Cedars-Sinai Medical Center, PubMed, G2, Capterra, Reddit
Last updated: 2026-07-18
Ratings & Reviews
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Press & Coverage
BioSpace
Catalyst MedTech Announces Channel Partnership with APQ Health to Expand Access to Advanced Cardiac CT Analysis
Catalyst MedTech partnered with APQ Health to broaden access to advanced cardiac CT angiography (CCTA) visualization and plaque analysis services, utilizing APQ Health's exclusive license for Cedars-Sinai's Autoplaqueu00ae AI software. This collaboration aims to streamline CCTA workflows and provide advanced, FDA-cleared plaque quantification and reporting to healthcare providers.
2026-07
Cedars-Sinai
AI Heart Risk Prediction during CT Scans
Cedars-Sinai researchers have developed and validated an FDA-approved AI-enabled software tool called Autoplaque, which analyzes cardiac CT angiography (CCTA) images to quantify dangerous soft plaque and stable calcified plaque, improving heart attack risk prediction. This tool represents a paradigm shift from solely measuring arterial narrowing to assessing plaque volume, which has been shown to be a stronger predictor of myocardial infarction.
2026-01
PMC (PubMed Central)
Deep Learningu2013based Automated Coronary Plaque Quantification
This peer-reviewed article discusses the latest generation of deep learning-based coronary analysis tools, including Autoplaque 3.0 from Cedars-Sinai Medical Center, which is a semi-automated, on-site, supervised-learning system for plaque quantification. The study highlights that deep learning-based plaque analysis significantly reduces the time required compared to expert manual analysis.
2025-08
PMC (PubMed Central)
Influence of plaque characteristics by coronary computed tomography angiography on lesion-specific ischemia: a systematic review and meta-analysis
This systematic review and meta-analysis, co-authored by a researcher from Cedars-Sinai Medical Center, investigates the association between plaque characteristics from coronary CT angiography (CCTA) and lesion-specific ischemia, utilizing software like Autoplaque v.2.9 for quantitative assessment. The findings suggest that high-risk plaque characteristics and larger plaque volumes are linked to ischemia, improving discrimination compared to stenosis evaluation alone.
2025-03
CMS.gov
Artificial Intelligence Enabled CT Based Quantitative Coronary Topography (AI-QCT)/Coronary Plaque Analysis (AI-CPA) (L39913)
This regulatory announcement from CMS discusses the coverage for Artificial Intelligence Enabled CT Based Quantitative Coronary Topography (AI-QCT)/Coronary Plaque Analysis (AI-CPA), noting that software to perform AI-CPA, such as Autoplaque (Version 2.5, Cedars-Sinai Medical Center), must be FDA cleared or approved. It also outlines clinical circumstances where AI-QCT/AI-CPA is not considered reasonable or necessary.
2024-10
Circulation: Cardiovascular Imaging (American Heart Association Journals)
Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis
This study details how deep learning can provide automated quantification of coronary plaque from CT angiography, with the model integrated into Autoplaque v3.0 (Cedars-Sinai Medical Center), an FDA-cleared software package. It determines per-patient age- and sex-specific distributions of deep learning-based plaque measurements and evaluates their risk prediction for myocardial infarction.
2024-09
FDA
Cedars-Sinai Medical Center Philip Won Hyman, Phelps & McNamara, P.C. 700 Thirteenth Street NW, Suite 1200 WASHINGTON DC
This FDA 510(k) summary provides details on Cedars-Sinai Medical Center's Autoplaque 3.0, a stand-alone software for post-processing coronary CT angiography images to measure stenosis and characterize coronary plaque and artery remodeling. It is intended for use by cardiologists and radiologists as an interactive diagnostic tool.
2023-05
dot.LA
Artificial Intelligence Will Change How Doctors Diagnose
Cedars-Sinai unveiled Autoplaque, a new AI-enabled device that can identify and measure arterial plaque significantly faster than physicians, aiding in the prediction of heart attacks. This tool aims to drastically reduce the time needed for heart scan analysis, allowing for more proactive patient care.
2022-03
Videos
Product demos, reviews, and walkthroughs for Autoplaque.
No videos found. Search YouTube directly
Frequently Asked Questions
Autoplaque is a workstation-based post-processing application designed to analyze coronary CT angiography (CCTA) data. It automates the detection, quantification, and characterization of coronary plaques and stenoses, providing rapid and consistent analysis that can be time-consuming and imprecise with manual evaluation. This allows physicians to gain a clearer understanding of heart disease risk and guide personalized treatment plans.
Autoplaque is an FDA-cleared diagnostic reading software. As an FDA-cleared medical device handling patient data, it is subject to stringent regulations, including those related to data privacy and security. Users are also responsible for ensuring appropriate monitor quality and ambient light conditions for clinical applications.
Autoplaque is backed by numerous peer-reviewed publications and has been involved in multicenter, international trials and studies, including the Landmark SCOT-HEART clinical trial. AI-driven models for coronary plaque analysis demonstrate high diagnostic accuracy for plaque detection, segmentation, quantification, and vulnerability assessment, often achieving high agreement with expert readers while significantly reducing analysis time.
Traditional methods involve manual evaluation of CCTA images, which can be time-consuming and prone to inter-observer variability. Autoplaque differentiates itself by providing automated, rapid, and consistent quantification of total and high-risk plaque volumes and composition, which is often not feasible manually. It also helps reduce variability between readers, ensuring more consistent and reliable results.
While specific pricing models for Autoplaque are not detailed, AI-enabled coronary plaque analysis is increasingly covered by insurance. New Category I CPT codes, such as 75577, have been established for automated quantification and characterization of coronary atherosclerotic plaque, with Medicare assigning reimbursement rates for both hospital outpatient and physician office settings.
Limitations for AI plaque analysis include the requirement for high-quality CCTA images, as plaque brightness can vary with technical factors like scanner type and scan performance. Broader challenges for AI in clinical adoption include data heterogeneity, algorithmic bias, limited model transparency, and insufficient prospective validation in diverse patient populations. The software is not intended to replace the skill and judgment of a qualified medical practitioner.
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| Last Verified: 2026-04-18 | First Added: 2026-04-18