Autoplaque vs KardiaAI

Similar category, different focus. These tools serve overlapping but distinct needs. Comparability 60/100 Comparability is an AI-graded 0–100 score of how directly these two tools compete — higher means a more apples-to-apples comparison.

Autoplaque

by APQ Health (formerly Cedars-Sinai Medical Center: AIM)

KardiaAI

by AliveCor

AI Verdict

Autoplaque specializes in the quantitative analysis of coronary plaques and luminal stenoses from CT angiography images, aiding in heart attack risk prediction. KardiaAI, on the other hand, focuses on analyzing ambulatory ECG rhythms to detect various cardiac conditions such as AFib, bradycardia, and tachycardia. While both are cardiology AI tools, they address different aspects of cardiac diagnostics using distinct data modalities.

Choose Autoplaque if…

Choose Autoplaque if your primary need is a detailed, quantitative assessment of coronary artery disease and plaque burden from CT angiography images. This tool is ideal for clinicians who require precise measurements of luminal stenoses and characterization of atherosclerotic plaque to inform heart attack risk prediction and guide treatment strategies.

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Choose KardiaAI if…

Choose KardiaAI if you require an AI-powered solution for analyzing ambulatory ECG rhythms to detect a wide range of cardiac arrhythmias. This tool is best suited for healthcare providers or systems looking to integrate a medical-grade AI library into mobile or web platforms for continuous or on-demand heart rhythm monitoring and rapid identification of conditions like AFib, bradycardia, and tachycardia.

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Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Quick Comparison

Feature	Autoplaque	KardiaAI
Pricing	Unknown	Subscription-based for advanced features and services.
Deployment	Workstation-based post-processing application, runs on Windows or Mac OS.	Mobile app, cloud-based.
BAA Available	Unknown	Yes AI-estimated
FDA Status	Cleared AI-estimated	Cleared AI-estimated
HIPAA	Unknown	Yes AI-estimated

Head-to-Head

AI-generated assessment across six dimensions based on each tool's documented features and compliance posture. Grounded in public data — not a substitute for hands-on evaluation.

Usability

KardiaAI

KardiaAI, deployed as an API library integrated into mobile apps and web platforms, offers broader and more accessible usability for a wider range of users, including patients and general practitioners. Autoplaque, as workstation-based software, is likely designed for specialized users in a clinical imaging environment, requiring dedicated hardware and expertise.

Clinical Value

Autoplaque

Both tools offer significant clinical value as FDA-cleared solutions. However, Autoplaque's AI-powered quantitative analysis of coronary plaques and luminal stenoses from CT angiography provides a deeper, prognostic insight into heart attack risk prediction and treatment guidance, addressing a critical need in preventative cardiology and intervention planning. KardiaAI focuses on real-time arrhythmia detection, which is also vital but more diagnostic of current electrical activity.

Pricing & Value

KardiaAI

KardiaAI offers transparent, subscription-based pricing with clear tiers, allowing potential users to easily understand the cost and value proposition. Autoplaque's pricing is not disclosed, making it impossible to assess its value without further information, which generally indicates less transparency for the customer.

Enterprise Readiness

KardiaAI

KardiaAI explicitly states HIPAA compliance and the availability of a Business Associate Agreement (BAA), which are fundamental requirements for enterprise adoption in healthcare. Its API-based deployment also facilitates easier integration into existing health system workflows. Autoplaque lacks explicit statements regarding HIPAA compliance or BAA in the provided information, and its workstation-based deployment may pose integration challenges for large enterprises.

Innovation

Autoplaque

Autoplaque demonstrates a high degree of innovation through its specialized AI application for quantitative analysis of coronary plaques and luminal stenoses from complex CT angiography images, including automated segmentation. While AI-powered ECG analysis by KardiaAI is valuable, Autoplaque's focus on detailed structural analysis for prognostic insights represents a more novel and complex application of AI in cardiovascular imaging.

Support & Docs

KardiaAI

AliveCor, the company behind KardiaAI, is a commercial entity with established consumer and professional product lines, offering extensive support resources including FAQs, user manuals, and direct contact options. Autoplaque, developed by a research group within Cedars-Sinai, does not have readily available public documentation or a formalized commercial support structure, making KardiaAI the clear winner in this dimension.

Feature-by-Feature

Detail beyond the Quick Comparison summary. For pricing, deployment, BAA, FDA, and HIPAA see the Overview tab.

Feature	Autoplaque	KardiaAI
Name	Autoplaque	KardiaAI
Company	Cedars-Sinai Medical Center: AIM	AliveCor
Primary Data Input/Modality	CT angiography images	Ambulatory ECG rhythms
Core Analytical Function	Quantitative analysis of coronary plaques and luminal stenoses	Detection of atrial fibrillation, bradycardia, tachycardia, and normal sinus rhythm from ECG
Target Specialties	Cardiology, Radiology	Cardiology, Emergency Medicine, Internal Medicine

Videos

Demos, reviews, and walkthroughs featuring Autoplaque and KardiaAI.

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Frequently Asked Questions

How do Autoplaque and KardiaAI integrate into existing clinical workflows, and for which specific cardiac conditions are they primarily intended?

Autoplaque is designed for post-processing CT angiography images to quantitatively analyze coronary plaques and stenoses, aiding in heart attack risk prediction within a radiology or cardiology imaging workflow. KardiaAI, an AI software library, analyzes ambulatory ECG rhythms to detect conditions like AFib, bradycardia, and tachycardia, fitting into rhythm monitoring and emergency medicine workflows.

What is the typical depth of EHR integration offered by each tool for seamless data transfer and reporting?

Autoplaque, as workstation-based software, allows for standardized characterization of plaque and stenosis from DICOM image data, which can be loaded from a local computer hard drive. KardiaAI offers an API library and SDK for integration into mobile apps and web platforms, and its KardiaPro platform can integrate with EHR systems like Epic for automated data transfer and reporting.

What level of accuracy and clinical validation can physicians expect from each tool for their respective analyses?

Autoplaque provides AI-powered quantitative analysis of coronary plaques and stenoses, with studies showing excellent correlation with predicate devices for plaque volume measurements and strong agreement with intravascular ultrasound (IVUS). KardiaAI offers medical-grade accuracy for heart rhythm data, with studies demonstrating high sensitivity and specificity in detecting conditions like atrial fibrillation, and its KAI 12L AI technology can detect 35 cardiac determinations.

What is the expected learning curve for clinicians to effectively use each tool, particularly regarding interaction with automated analyses?

For Autoplaque, clinicians will need to learn to review and potentially edit the automated vessel, plaque, and lumen segmentations, as the software is intended for use by appropriately trained medical professionals. For KardiaAI, as an API library, the learning curve would primarily depend on the user interface of the mobile app or web platform it's integrated into, with the Kardia 12L ECG System requiring minimal self-guided training.

What audit trail features are available in each tool to ensure traceability of analyses, clinician reviews, and any modifications made to the results?

While specific audit trail features are not explicitly detailed for Autoplaque, its design allows for clinician review and editing of automated segmentations, implying a need for tracking these interactions. KardiaAI, as a medical-grade AI solution integrated into various platforms, would typically incorporate mechanisms to log analysis events and results within its integrated systems to ensure traceability.

What are the key considerations for deploying and managing these tools across multiple clinical sites or hospital systems?

Autoplaque, as workstation-based software, would likely require individual installations and management per workstation or site, running on Windows or Mac computer platforms. KardiaAI, being an API library, could potentially be integrated into a centralized platform, simplifying management across multiple sites, with AliveCor's enterprise platform allowing third-party providers to manage patients' heart conditions with easy integration.

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Backers

Who funded each tool's parent company.