Automated Deep Learning Pipeline for Characterizing Left Ventricular Diastolic Function.

ABSTRACT:

Introduction

Left ventricular diastolic dysfunction (LVDD) is most commonly evaluated by echocardiography. However, without a sole identifying metric, LVDD is assessed by a diagnostic algorithm relying on secondary characteristics that is laborious and has potential for interobserver variability.

Methods

To characterize concordance in clinical evaluations of LVDD, we evaluated historical echocardiogram studies at two academic medical centers for variability between clinician text reports and assessment by 2016 American Society of Echocardiography (ASE) guidelines. We then developed a workflow of 8 artificial intelligence (AI) models trained on over 155,000 studies to automate assessment of LVDD. Model performance was evaluated on temporally distinct held-out test sets from t

SUBMITTER: Yuan V 

PROVIDER: S-EPMC12060936 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature