ECG-LEARNING: Deep Learning for Intelligent Identification of Arrhythmias

Sponsor

First Affiliated Hospital Xi'an Jiaotong University (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT05967546

Collaborator

521 Hospital of NORINCO Group (Other), Shaanxi Provincial People's Hospital (Other), Xiangyang Central Hospital (Other)

4,000

Enrollment

Location

62.1

Anticipated Duration (Months)

64.5

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

This study aims to design and train a deep learning model for the diagnosis of different arrhythmias.

Condition or Disease	Intervention/Treatment	Phase
Arrhythmia	Other: Observational

Detailed Description

This study aims to retrospectively and prospectively collect routine clinical data such as electrocardiograms from patients with arrhythmias who meet the inclusion and exclusion criteria. Then we will design and train a deep learning model to analyse the electrocardiographic features of the arrhythmias, and identify the types of arrhythmias and evaluate the value of the model for the diagnosis of different arrhythmias.

Study Design

Study Type:

Observational

Anticipated Enrollment :

4000 participants

Observational Model:

Cohort

Time Perspective:

Other

Official Title:

Deep Learning for Intelligent Identification of Arrhythmias (ECG-LEARNING): an Investigator-initiated, National Multicenter, Retrospective-prospective, Cohort Study

Anticipated Study Start Date :

Oct 30, 2023

Anticipated Primary Completion Date :

Aug 31, 2028

Anticipated Study Completion Date :

Dec 31, 2028

Arms and Interventions

Arm	Intervention/Treatment
Experimental Group ECG data and clinical data from this group of arrhythmia patients will be used to build a deep learning model.	Other: Observational No interventions will be given to patients.

Arm

Intervention/Treatment

Experimental Group

ECG data and clinical data from this group of arrhythmia patients will be used to build a deep learning model.

Other: Observational

No interventions will be given to patients.

Outcome Measures

Primary Outcome Measures

A deep learning model designed to intelligently identify the types of arrhythmia. [1 day after the enrollment.]
Deep learning was used to develop diagnostic models and intelligently identify arrhythmia types.

Secondary Outcome Measures

The sensitivity, specificity and accuracy of the deep learning model [1 day after the enrollment.]
The sensitivity, specificity and accuracy of a deep learning model designed were evaluated by intracardiac electrophysiological examination results to identify patients with arrhythmia from various centers.

Eligibility Criteria

Criteria

Ages Eligible for Study:

3 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

For retrospective study: 1.Patients with arrhythmia diagnosed by routine surface 12-lead electrocardiogram or Holter; 2.The type of arrhythmia is diagnosed by intracardiac electrophysiological examination.
For prospective study: 1.Patients with arrhythmia diagnosed by routine surface 12-lead electrocardiogram or Holter; 2.Intracardiac electrophysiological examination is planned.

Exclusion Criteria:

Lack of routine surface 12-lead electrocardiogram or holter data;
Lack of intracardiac electrophysiological examination;
Patients refused to sign informed consent and refused to participate in the study.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	First Affiliated Hospital of Xi'an Jiantong University	Xi'an	Shaanxi	China	710061

Sponsors and Collaborators

First Affiliated Hospital Xi'an Jiaotong University
521 Hospital of NORINCO Group
Shaanxi Provincial People's Hospital
Xiangyang Central Hospital

Investigators

Principal Investigator: Guoliang Li, M.D., First Affiliated Hospital Xi'an Jiaotong University

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

First Affiliated Hospital Xi'an Jiaotong University

ClinicalTrials.gov Identifier:

NCT05967546

Other Study ID Numbers:

XJTU1AF2023LSK-170

First Posted:

Aug 1, 2023

Last Update Posted:

Aug 1, 2023

Last Verified:

Jun 1, 2023

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Keywords provided by First Affiliated Hospital Xi'an Jiaotong University

Arrhythmia

Additional relevant MeSH terms:

Arrhythmias, Cardiac

Study Results

No Results Posted as of Aug 1, 2023