EchoSolv
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About
EchoSolv is an advanced AI platform designed to enhance the diagnosis of structural heart disease through deep learning techniques. It utilizes the largest database of echocardiograms linked to mortality, providing unparalleled speed and accuracy in identifying patients at risk. EchoSolv improves clinical workflows by prioritizing urgent referrals, automatically detecting at-risk patients, and generating comprehensive reports. Its comprehensive and unbiased approach excludes common clinical biases, ensuring consistent and personalized care. The platform offers various features, including real-time alerts, patient echo history management, and quality assurance checks. With its innovative technology, EchoSolv empowers clinicians to deliver timely and effective healthcare, ultimately saving lives. Seamless integration with existing systems and high-level data security ensure that clinical teams can rely on EchoSolv to manage their workflows efficiently while safeguarding sensitive patient information.
Platform
Features
• real-time alerts
• integration with pacs systems
• comprehensive patient discovery
• patient echo history management
• automated detection of at-risk patients
• secure data handling with hipaa compliance
• stealth mode for presentations
• flexible reporting options
FAQs
How does your AI work?
Echocardiographic data is analyzed using a proprietary algorithm based on the largest echocardiogram database globally to assess risk.
How long does it take to give me a result?
Real-time assessments take about 1.5 seconds, and bulk historical assessments can be done in a few minutes.
What's the specificity and sensitivity?
EchoSolv™ has 99.98% accuracy as a decision-support tool for risk stratification.
Why is EchoSolv™ AI better at detecting patients with aortic stenosis?
EchoSolv™ maps all measurements across studies into a multi-dimensional AI model to assess severe aortic stenosis risk more comprehensively.
What is the minimum level of measurements required for accurate assessment?
Minimum data required includes age, BMI, LV Ejection Fraction, and Peak Aortic Velocity.
How do you correct for a poor sonographer study?
The AI corrects for inaccuracies by using a multiple imputation model trained on extensive echocardiogram data.
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