Automated Evaluation for Pericardial Effusion and Cardiac Tamponade with Echocardiographic Artificial Intelligence.

ABSTRACT:

Background

Timely and accurate detection of pericardial effusion and assessment cardiac tamponade remain challenging and highly operator dependent.

Objectives

Artificial intelligence has advanced many echocardiographic assessments, and we aimed to develop and validate a deep learning model to automate the assessment of pericardial effusion severity and cardiac tamponade from echocardiogram videos.

Methods

We developed a deep learning model (EchoNet-Pericardium) using temporal-spatial convolutional neural networks to automate pericardial effusion severity grading and tamponade detection from echocardiography videos. The model was trained using a retrospective dataset of 1,427,660 videos from 85,380 echocardiograms at Cedars-Sinai Medical Center (CSMC) to predict PE sev

SUBMITTER: Chiu IM 

PROVIDER: S-EPMC11623744 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature