ArrhythmiaECG-LEARNINGNCT05967546
Deep Learning for Intelligent Identification of Arrhythmias
An observational study of Arrhythmia, run by First Affiliated Hospital Xi'an Jiaotong University at 1 site in China.
Trial summary
This study aims to design and train a deep learning model for the diagnosis of different arrhythmias. Read more about this study
- Who is it for?
- Ages 3 years and older
- Conditions the study is for
- Arrhythmia
- What is being tested?
- Observational
- How many people
- 4,000 participants
- Where
- 1 siteChina
- Study dates
- 30 Dec 2024to 31 Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 3 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients diagnosed with arrhythmia by twelve-lead electrocardiogram or Holter.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Chaofeng Sun, M.D. contact
China1 site
First Affiliated Hospital of Xi'an Jiantong University
Xi'an, Shaanxi, 710061
About this study
Official title: Deep Learning for Intelligent Identification of Arrhythmias (ECG-LEARNING): an Investigator-initiated, National Multicenter, Retrospective-prospective, Cohort Study
This study aims to design and train a deep learning model for the diagnosis of different arrhythmias.
Read the 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.
- Conditions
- Arrhythmia
- Being tested
- Observational
- Keywords
- Arrhythmia
- Medical subject headings
- Arrhythmias, Cardiac
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ObservationalOther
No interventions will be given to patients.
| Study group | Role | What they receive |
|---|---|---|
| Experimental Group | ECG data and clinical data from this group of arrhythmia patients will be used to build a deep learning model. Receives: Other: Observational |
What is being measured
The primary outcome is the main question the study is designed to answer.
A deep learning model designed to intelligently identify the types of arrhythmia.
The model is trained on the training set, the best model and hyperparameters are selected through the verification set, and finally the model results are tested on the test set.
Measured: 1 day after the enrollment.
Secondary outcomes (1)
The sensitivity, specificity and accuracy of the deep learning model
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.
Measured: 1 day after the enrollment.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Aug 2023
- Study start30 Dec 2024 (estimated)
- Primary completion31 Aug 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ArrhythmiaStudies of the same condition, open and under way.
- More trials run by First Affiliated Hospital Xi'an Jiaotong UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 4,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- 521 Hospital of NORINCO Group, Shaanxi Provincial People's Hospital, Xiangyang Central Hospital
- Responsible party
- Sponsor
- Investigators
- Guoliang Li, M.D., Principal investigator, First Affiliated Hospital Xi'an Jiaotong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05967546
- Sponsor's protocol ID
- XJTU1AF2023LSK-170
- Acronym
- ECG-LEARNING
- Status last verified
- Apr 2024
- First submitted
- 6 Jul 2023
- Last updated
- 4 Apr 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning for Intelligent Identification of Arrhythmias (ECG-LEARNING): an Investigator-initiated, National Multicenter, Retrospective-prospective, Cohort Study
- ClinicalTrials.gov ID
- NCT05967546
- Acronym / sponsor ID
- ECG-LEARNING / XJTU1AF2023LSK-170
- Conditions
- Arrhythmia
- Keywords
- Arrhythmia
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- First Affiliated Hospital Xi'an Jiaotong UniversityUniversity, hospital or other
- Collaborators
- 521 Hospital of NORINCO Group, Shaanxi Provincial People's Hospital, Xiangyang Central Hospital
- Study start
- 30 Dec 2024 (estimated)
- Primary completion
- 31 Aug 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 1 Aug 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to design and train a deep learning model for the diagnosis of different arrhythmias.
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
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Experimental Group | – | Other: ObservationalECG data and clinical data from this group of arrhythmia patients will be used to build a deep learning model. |
Who may qualify
- Ages
- 3 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients diagnosed with arrhythmia by twelve-lead electrocardiogram or Holter.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| ObservationalNo interventions will be given to patients. | Other | Experimental Group |
Primary outcome
A deep learning model designed to intelligently identify the types of arrhythmia.
Time frame: 1 day after the enrollment.
The model is trained on the training set, the best model and hyperparameters are selected through the verification set, and finally the model results are tested on the test set.
1 secondary outcome
The sensitivity, specificity and accuracy of the deep learning model
Time frame: 1 day after the enrollment.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Apr 2024.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Chaofeng Sun, M.D. contact
Guoliang Li, M.D. principal investigator
First Affiliated Hospital Xi'an Jiaotong University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Arrhythmia, run by First Affiliated Hospital Xi'an Jiaotong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 4 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Guoliang Li, M.D. (+8613759982523, liguoliang_med@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
A deep learning model designed to intelligently identify the types of arrhythmia. (time frame: 1 day after the enrollment.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Apr 2024.