A generalized deep learning model for heart failure diagnosis using dynamic and static ultrasound.

ABSTRACT:

Objective

Echocardiography (ECG) is the most common method used to diagnose heart failure (HF). However, its accuracy relies on the experience of the operator. Additionally, the video format of the data makes it challenging for patients to bring them to referrals and reexaminations. Therefore, this study used a deep learning approach to assist physicians in assessing cardiac function to promote the standardization of echocardiographic findings and compatibility of dynamic and static ultrasound data.

Methods

A deep spatio-temporal convolutional model r2plus1d-Pan (trained on dynamic data and applied to static data) was improved and trained using the idea of "regression training combined with classification application," which can be generalized to dynamic ECG and static cardi

SUBMITTER: Liu Z 

PROVIDER: S-EPMC10680380 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature