CardioVision
by Icardio.Ai
· Based in United States →
— Automating Ultrasound with Artificial Intelligence for Cardiovascular Diagnostics
Tiered pricing with a base charge and per-scan fees.
Regulatory Status Disclosed
Overview
CardioVision by iCardio.ai is an FDA 510(k) cleared artificial intelligence (AI) software designed as a diagnostic aid for the detection of aortic stenosis (AS). It is intended for use by interpreting clinicians to support the determination of the likelihood of clinically significant aortic stenosis.
- What it does: CardioVision analyzes a single cine-loop DICOM of the parasternal long axis (PLAX) view from an echocardiogram. Using deep learning, it provides a binary classification of “none/mild” or “moderate/severe,” indicating whether the echocardiogram is consistent with moderate or severe aortic stenosis. The system can also identify when image quality is insufficient, resulting in an “indeterminate” output.
- Who it is for: This tool is for cardiologists and other interpreting clinicians in care settings where echocardiographic exams are performed, including outpatient and primary care environments. It is indicated for use in adult populations over 21 years of age.
- How it fits a clinical or practice workflow: CardioVision is a standalone image analysis software that integrates with existing medical imaging workflows, including PACS, DICOM viewers, and imaging worklists. It provides adjunctive information to assist clinicians in diagnosing AS, with the final diagnosis remaining the responsibility of the interpreting clinician, who should consider all patient data.
- Notable capabilities: CardioVision has received FDA 510(k) clearance and was granted FDA Breakthrough Device Designation for its capability to automatically detect aortic stenosis. It is designed to function effectively even with varying image quality, including in outpatient imaging environments.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered detection of moderate to severe Aortic Stenosis
- Input: DICOM-compliant echocardiogram (parasternal long-axis view)
- Output: Binary classification of 'none/mild' or 'moderate/severe' aortic stenosis
- Functions as an adjunctive diagnostic aid for interpreting clinicians
- Utilizes deep learning for prediction without explicit variable measurement
- Integrates into existing PACS workflows
- Viable for use with handheld ultrasound devices
Use Cases
- Early detection and screening for aortic stenosis
- Assisting clinicians in diagnosing aortic stenosis from echocardiograms
- Improving diagnostic accuracy and efficiency in cardiovascular diagnostics
- Supporting clinical decision-making in outpatient settings, such as primary care
- Reducing diagnostic variability in echocardiogram interpretation
What Physicians Need to Know
Echocardiography AI Features
CardioVision™ by iCardio.ai is an FDA-cleared AI software designed to aid in the detection of aortic stenosis from echocardiogram clips. It utilizes deep learning to predict aortic stenosis without explicit measurement of variables. The software processes DICOM-compliant transthoracic cardiac ultrasound images, providing automated view classification, quality checks, and estimations of cardiac anatomical measurements and quantities. iCardio.ai's offerings also include EchoMeasure™ for automated linear and volumetric measurements, EchoCheck for image quality and completeness tracking, and EchoGPT for image-to-text descriptions of ultrasound findings. The AI can predict abnormal left ventricular function with reported higher accuracy than human interpretation. It also features real-time labeling of heart features during ultrasound, designed for edge devices and suboptimal imaging conditions.
Cardiac CT/MRI Analysis
While CardioVision™ primarily focuses on echocardiography, AI tools in cardiology are expanding to include cardiac CT analysis for various conditions. For example, AI-driven fractional flow reserve hemodynamic flow measurements derived from noninvasive CT imaging (FFR-CT) are included in ACC/AHA 2021 Chest Pain Guidelines. Other AI tools are being developed to predict heart failure using routine cardiac CT scans by analyzing textural changes in fat around the heart. There are also AI-driven pipelines for medical image segmentation and geometry reconstruction for aortic stenosis using CT data, which perform calcification analysis.
Heart Failure Risk Prediction
While CardioVision™ focuses on aortic stenosis detection, AI models applied to ECGs can identify individuals at elevated risk of heart failure. These AI-ECG models have shown the ability to predict new-onset heart failure across diverse populations. Additionally, AI tools are being developed to predict heart failure using cardiac CT scans by identifying subtle changes in perivascular fat. AI techniques applied to cardiac ultrasound data combined with electronic health records can also predict advanced heart failure with high accuracy.
Coronary Artery Assessment
While CardioVision™ focuses on echocardiography, other AI-powered prototypes are being developed to estimate coronary artery disease risk using machine learning, patient clinical data, and interactive 3D heart visualization. These tools aim to provide vessel-level risk probability estimation and explainable AI for better understanding of predictions.
Cardiac Monitoring Integration
iCardio.ai's solutions are designed for integration into existing PACS networks and hardware devices. The company has partnerships with various systems, including UltraLinQ Healthcare Solutions, deepc, HeartLab, and SARC MedIQ, to integrate its AI into imaging systems. There is also a multi-year collaboration with GE Healthcare to integrate iCardio.ai's technology into GE ultrasound platforms.
Physician Tip
CardioVision™ can serve as a valuable diagnostic aid for detecting aortic stenosis, especially in outpatient settings where image quality may vary. The automated measurements and quality checks offered by iCardio.ai's suite of tools can enhance efficiency and consistency in echocardiogram interpretation, potentially reducing sonographer workload and variability. While the AI provides automated analysis, clinicians retain the option to accept, reject, edit, or ignore the output, maintaining physician oversight in clinical decision-making. The ability of the AI to function on edge devices and in sub-par imaging conditions suggests its potential utility in diverse clinical environments, including point-of-care ultrasound.
iCardio.ai's solutions, including CardioVision™, are designed for seamless integration into existing PACS and hardware systems through APIs. The company has established partnerships with ultrasound manufacturers and healthcare systems, including GE Healthcare, UltraLinQ, deepc, HeartLab, and SARC MedIQ, to broaden the accessibility and utility of its echocardiography AI. This allows for automated analysis to be incorporated directly into standard reporting workflows, streamlining the echocardiography review process.
Details
| Category | Cardiology AI |
| Pricing | Tiered pricing with a base charge and per-scan fees. |
| Deployment | Integrates into existing PACS workflows; built for PACS and hardware. |
| Compliance | |
| BAA Available | Unknown |
| HIPAA Compliant | Unknown |
| FDA Status | Cleared AI-estimated |
| Integrations | |
| EHR | Not specified |
| Specialties | Cardiology, Radiology |
What the Web Says
CardioVision by iCardio.ai is an FDA 510(k) cleared AI software designed to assist in the detection of aortic stenosis from echocardiography images. It has also received FDA Breakthrough Device Designation for its capability to automatically detect aortic stenosis, particularly in settings where it might be missed. The technology aims to streamline and automate the generation of patient-specific models for aortic stenosis, which can be used to simulate virtual interventional procedures and predict clinical outcomes.
Overall: PositiveStrengths
- Automated detection of aortic stenosis with high sensitivity and specificity, even in outpatient imaging environments with variable image quality.
- Potential to reduce human error and inter-observer variability in echocardiography measurements.
- Aids in early and accurate diagnosis, facilitating timely interventions and potentially improving patient outcomes.
- Can expand the reach of life-saving technology to primary care and underserved populations, especially with handheld ultrasound devices.
- Integrates into existing cardiac imaging and reporting workflows, potentially streamlining the process.
- Leverages a large proprietary dataset of over 200 million ultrasound images for its AI models.
Limitations
- No specific cons were found in the provided search results from physicians, healthcare IT, tech reviewers, Reddit, G2, or Capterra.
- Reddit discussions about AI in ECG interpretation generally raise concerns about accuracy, training, trust in the technology, and medico-legal liability, though these are not specific to CardioVision.
Based on reviews from: cardiacwire.com, nih.gov, reddit.com, icardio.ai, innolitics.com, medaxiom.com, cardiovascularbusiness.com, saashub.com, g2.com, physicianaitools.com, nvidia.com, capterra.com
Last updated: 2026-10-30
Ratings & Reviews
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Press & Coverage
icardio.ai
iCardio.ai Receives Second FDA 510(k) Clearance, CardioVision™ for Aortic Stenosis
iCardio.ai announced it received FDA 510(k) clearance for its CardioVision™ software, which automatically detects aortic stenosis from echocardiograms. This marks the company's second FDA clearance and is intended to improve diagnosis and treatment for the 1.5 million Americans affected by the disease.
2025-11
icardio.ai
iCardio.ai Receives FDA Breakthrough Designation for Automated Detection of Aortic Stenosis
iCardio.ai was granted Breakthrough Device Designation by the FDA for its machine-learning system designed to detect moderate and severe Aortic Stenosis (AS) using handheld ultrasound devices. This designation highlights the technology's potential to address critical diagnostic gaps and improve treatment pathways for cardiac patients.
2024-02
icardio.ai
iCardio.ai Joins Butterfly Garden to Deploy Its Cardiac AI Suite on Butterfly Network's Imaging Platform
iCardio.ai announced its collaboration with Butterfly Network, joining the Butterfly Garden AI Marketplace to develop and deploy its cardiac ultrasound AI tools for use with Butterfly's handheld ultrasound system. This partnership aims to integrate iCardio.ai's AI capabilities with Butterfly's portable ultrasound technology, making cardiac ultrasound more accessible.
2023-11
icardio.ai
iCardio.ai Partners with deepc for European Distribution of AI Algorithms
iCardio.ai announced a partnership with deepc, a European AI algorithm distributor, to integrate its AI-driven ultrasound interpretation solution into deepc's platform. This collaboration aims to accelerate echocardiogram interpretation time and enhance diagnostic precision for healthcare professionals in Europe.
2024-02
covefund.com
iCardio.ai Closes $4.5M Strategic Financing Led by Cedars-Sinai
iCardio.ai secured $4.5 million in strategic financing led by Cedars-Sinai Technology Ventures to advance its mission of integrating artificial intelligence into echocardiography. This funding also provides iCardio.ai with access to Cedars-Sinai's extensive cardiac imaging dataset, enhancing its data foundation.
2026-04
asecho.org
iCardio.ai Wins ASE 2026 Shark Tank Innovation Competition
iCardio.ai was awarded the top prize at the American Society of Echocardiography's (ASE) 2026 Shark Tank Innovation Competition. The company was recognized for its progress and development roadmap in translating its technology innovations into a sustainable business model, particularly for its EchoMeasure product.
2026-06
innolitics.com
Cardiovascular AI/ML SaMD: 97 cleared devices, two decades, signals over pixels
Innolitics reported that iCardio.ai cleared CardioVision (K251293) in 2025, noting its operation on a single PLAX view in outpatient settings and its targeting of primary-care POCUS. The article discusses the landscape of FDA-cleared cardiovascular AI/ML SaMD devices.
2026-04
cardiovascularbusiness.com
The latest FDA-cleared AI models in cardiology
Cardiovascular Business highlighted iCardio.ai's CardioVision as one of the FDA-cleared AI models in cardiology, specifically for its automated detection of moderate or severe aortic stenosis on echo results. The article provides an overview of 29 new cardiology AI products cleared by the FDA between June and December 2025.
2026-03
Videos
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Frequently Asked Questions
iCardio.ai CardioVision™ is an FDA-cleared AI software designed as a diagnostic aid for detecting Aortic Stenosis. It analyzes a parasternal long axis (PLAX) clip from an echocardiogram to determine the likelihood of clinically significant aortic stenosis, utilizing deep learning without explicitly measuring variables.
iCardio.ai CardioVision™ is FDA-cleared and has received Breakthrough Device designation from the FDA for its ability to automatically detect aortic stenosis. Practices should ensure their overall operations, including the use of AI tools, comply with HIPAA regulations, particularly regarding data security, access controls, and patient privacy.
While CardioVision specifically focuses on AI-driven detection of aortic stenosis from echocardiograms, other diagnostic methods for heart conditions include traditional echocardiography, stress tests, cardiac MRI, and nuclear perfusion imaging. Other AI tools are emerging in cardiology, with some focusing on different aspects like cardiac amyloid detection or broader cardiac imaging analysis.
CardioVision is indicated as a diagnostic aid to support the detection of Aortic Stenosis from a parasternal long axis (PLAX) clip. It's important to remember that AI tools are aids and clinical judgment remains paramount. The performance of AI in real-world outpatient settings, where image quality can vary, has shown high sensitivity and specificity.
While specific integration details for iCardio.ai CardioVision™ are not provided, generally, cardiology AI tools aim to integrate with EHR and CVIS to streamline workflows and provide a comprehensive view of patient data. Effective integration allows for seamless data exchange, improved diagnostic efficiency, and accelerated clinical workflows.
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| Last Verified: 2026-04-18 | First Added: 2026-04-18