Use of artificial intelligence to predict outcomes in mild aortic valve stenosis.

ABSTRACT:

Aims

Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.

Methods and results

A comprehensive database including 9611 patients with serial transthoracic echocardiograms was collected from a single institution across three clinical sites. The data set included parameters from echocardiograms, electrocardiograms, laboratory values, and diagnosis codes. Data from a single clinical site were preserved as an independent test group. Machine learning models were trained

SUBMITTER: Julakanti RR 

PROVIDER: S-EPMC11750192 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature