EchoSolv™ AS clinical highlights
Numerous clinical studies support its effectiveness for AS
Summary:
Without relying on left ventricular outflow tract measurements, artificial intelligence decision support algorithm (AI-DSA) used echocardiographic reports to reliably identify probability of severe aortic stenosis. These results suggest the possible utility for AI-DSA to enhance detection of severe AS individuals at risk for adverse outcomes.
Summary:
This study suggests an artificial intelligence automated alert system (AI-AAS) application that is agnostic to gender, hemodynamic bias, symptoms, or clinical factors; provides an objective alert to severe forms of AS (including guideline-defined severe AS) following a routine echocardiogram; and has the potential to increase the number of people (especially women) directed toward more definitive treatment/specialist care.
https://www.sciencedirect.com/science/article/pii/S2666602224001289?via=ihub
Summary:
An artificial intelligence decision support algorithm (AI-DSA) can identify the echocardiographic measurement characteristics of AS associated with poor survival (with not all cases guideline defined). Deployment of this tool in routine clinical practice could improve expedited identification of severe AS cases and more timely referral for therapy.
Summary:
This proof-of-concept study shows that AI measurement interpretation can identify severe AS by evaluating the entire phenotype, without reference to LVOT velocity or dimension. The AI performed equally in normal and impaired systolic function and in low-flow, low-gradient severe AS, accurately predicting future mortality risk independent of AS gradients.
https://www.sciencedirect.com/science/article/pii/S1936878X19310101?via=ihub