Atrial Fibrillation Detecting Software Gung Atrial Fibrillation Detecting Software

Study Overview
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Brief Summary
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Chang Gung Atrial Fibrillation Detection Software is an artificial intelligence electrocardiogram signal analysis software that detects whether a patient has atrial fibrillation by static 12-lead ECG signals. This study is a non-inferiority test based on the control group. The main purpose is to verify whether Chang Gung atrial fibrillation detection software can correctly identify atrial fibrillation in patients with atrial fibrillation, and can be used to provide a reference for doctors to detect atrial fibrillation.

Detailed Description
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This study is a retrospective study, and the data is from the six hospitals of Chang Gung Medical Research Database (CGRD). We collected de-identified static 12-lead electrocardiogram (ECG) data from the database during the period of January 1, 2006, to December 31, 2019. We created a training set and a testing set of ECG data from the CGRD. Then, we stratified and sampled ECG signals from the testing set according to the actual proportion to obtain the experimental sample. The computer first preliminarily screened and selected ECG data that met the inclusion and exclusion criteria, and then numbered them sequentially. A cardiologist confirmed that the sampled ECG data did not include exclusion criteria. The ECG data were converted into images and interpreted for the presence or absence of atrial fibrillation by three cardiologists. Their results were used as the gold standard (reference) for this study. After determining the experimental standards, the ECG signals were inputted into the Chang Gung Atrial Fibrillation Detection software for analysis and interpretation of each ECG data. After the software interpretation was completed, the results were compared with the interpretations of the physicians, and the primary and secondary evaluation indicators were analyzed accordingly.

Official Title
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A Study to Evaluate Accuracy and Validity of the Chang Gung Atrial Fibrillation Detecting Software

Conditions
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Atrial Fibrillation

Intervention / Treatment
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* Device: Chang Gung Atrial Fibrillation Detecting Software


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Equal or greater than twenty years old Static 12-lead electrocardiogram of General Electric MUSE XML format file. The data comes from the static 12-lead electrocardiogram device of General Electric (model MAC5500). The electrocardiogram signal is 500 Hz. The Alternating current (AC) filter of the electrocardiogram signal is 60 Hz. Exclusion Criteria: Cases used in the model development process. Lacks any electrode. Contain any electrode lacks a segment. Misplaced leads

Ages Eligible for Study
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Minimum Age: 20 Years
Maximum Age: 100 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Software diagnosis<br>Software diagnosis with gold standard of 3 doctors' consensus. | Device: Chang Gung Atrial Fibrillation Detecting Software<br>* This software is expected to be used in clinical testing to interpret the static 12-lead ECG of adults who are over 20 years old and suspected of having atrial fibrillation, detect whether there is a signal of atrial fibrillation, and output the results for clinicians Near-instant auxiliary diagnostic use.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensitivity | The rate of test results that correctly indicate the presence. | baseline | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Specificity | The rate of test results that correctly indicate the absence. | baseline | 
| Accuracy | The rate of all test results that correctly indicate. | baseline | 
| Area Under the receiver operating characteristic Curve | A graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. | baseline | 
| Positive predictive value | The proportions of positive results in statistics and diagnostic tests that are true positive results | baseline | 
| Negative predictive value | The proportions of negative results in statistics and diagnostic tests that are true negative results | baseline | 
| False positive rate | The rate of test result which wrongly indicates that a particular condition or attribute is present | baseline | 
| False negative rate | The rate of test result which wrongly indicates that a particular condition or attribute is absent | baseline | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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artificial intelligence