EchoSolv™
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About
EchoSolv™ is an AI-powered platform designed to enhance the diagnosis of structural heart disease. It leverages a vast database of echocardiograms to identify patients at risk of life-threatening conditions, such as severe aortic stenosis, diastolic dysfunction, and heart failure. The tool offers real-time risk assessment, integrates with existing PACS systems, and provides comprehensive patient discovery features. It's designed to be unbiased, comprehensive, and improve clinical workflow without adding complexity. EchoSolv™ is clinically validated and boasts high accuracy.
Platform
Features
• real-time alerts
• mobile access
• pacs integration
• heart failure identification
• diastolic dysfunction identification
• severe aortic stenosis identification
• patient echo history review
• automatic identification of at-risk patients
FAQs
How does your AI work?
Echocardiographic measurement data is passed into our proprietary algorithm which is based on learnings from the largest database of echocardiograms globally. The AI uses the echocardiographic measurements to determine the probability of the presence of AS phenotype (i.e at risk patients). The final output of the software considers both the AI probability of AS phenotype as well as evaluating the echocardiographic measurements against the European Society of Cardiology/European Association for Cardio-Thoracic Surgery guidelines.
How long does it take to give me a result?
Our software can be used in 2 ways. 1) We can perform real-time assessments via secure cloud API in 1.5 seconds on average. 2) We can perform bulk historical assessments (quality audit) in under a few minutes.
What's the specificity and sensitivity?
99.98% accuracy. EchoSolv™ is a decision-support tool (not in-vitro diagnostic) providing risk stratification to support cardiologist decision making.
Why is EchoSolv™ AI better at detecting patients with aortic stenosis than what we do today by scanning the EMR data and cardiologist interpretation?
EchoSolv™ takes the measurements captured across all images in the study and maps them into a multi-dimensional AI model. The AI considers what’s happening using aortic velocity and gradient. Instead of using the aortic valve area, it examines all remaining measurements (including ventricular, atrial, and pulmonary and right heart measurement data) to determine the impact on the aortic valve area. The software then provides a probability of severe aortic stenosis. This automatically takes all AS scenarios into account including low-flow aortic stenosis, paradoxical low flow low-gradient and normal-flow low-gradient AS. The software provides two outputs, firstly it provides an alert for all patients that meet the AHA guidelines for severe AS, secondly it provides a probability of the severe AS phenotype (i.e at risk patients), providing unique insights not provided by other methodologies.
What is the minimum level of measurements that you require to provide an accurate assessment?
Age, BMI, LV Ejection Fraction and Peak Aortic Velocity.
How do you correct for a poor sonographer study/bad measurement data?
The AI has been trained using multidimensional clusters with data from over 1,000,000 echocardiograms. Any missing data is imputed using a multiple imputation model that has been tested across the entire data set. Bad (i.e. inaccurate) measurement data is also imputed based on the variables from all the other datapoints that were captured.
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