Challenges and solutions of echocardiography generalization for deep learning: a study in patients with constrictive pericarditis.

ABSTRACT:

Purpose

The inherent characteristics of transthoracic echocardiography (TTE) images such as low signal-to-noise ratio and acquisition variations can limit the direct use of TTE images in the development and generalization of deep learning models. As such, we propose an innovative automated framework to address the common challenges in the process of echocardiography deep learning model generalization on the challenging task of constrictive pericarditis (CP) and cardiac amyloidosis (CA) differentiation.

Approach

Patients with a confirmed diagnosis of CP or CA and normal cases from Mayo Clinic Rochester and Arizona were identified to extract baseline demographics and the apical 4 chamber view from TTE studies. We proposed an innovative preprocessing and image generalization fr

SUBMITTER: Jeong J 

PROVIDER: S-EPMC10569796 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature