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The purpose of the current study is to verify the effectiveness of the artificial intelligence algorithm applied to the electrocardiogram as a potential screening tool for left ventricular systolic dysfunction.
The current investigators have developed an artificial intelligence (AI) algorithm based on 12-lead electrocardiogram (ECG) detecting left ventricular systolic dysfunction, through 364,845 ECGs from 148,547 patients. Then, when the model was tested retrospectively on 59,805 ECGs of 24,376 patients, the model performance expressed as an area under the receiver operating characteristic curve was 0.889 (95% CI 0.887-0.891).
The investigators are planning to prospectively validate the model's effectiveness as a potential screening tool for left ventricular systolic dysfunction.
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI algorithm conducted on 12-lead ECG and transthoracic echocardiography | Diagnostic Test | 12-lead ECG is performed for each patient. For 12-lead ECG, AITIALVSD (AI algorithm) analysis will be performed through a separate server. |
| Measure | Description | Time Frame |
|---|---|---|
| Area under the receiver operating characteristic curve (AUROC) | AI model performance detecting LVSD, expressed as an AUROC. As a diagnostic assistance for LVSD, an ROC curve expressed as sensitivity to (1-specificity) will be presented, and the accuracy of prediction will be confirmed by calculating the AUROC, which is the area below. | Through study completion, an average of 1 year |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity | AI model sensitivity detecting LVSD | Through study completion, an average of 1 year |
| Specificity | AI model sensitivity detecting patients with normal left ventricular systolic function |
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Inclusion Criteria:
Exclusion Criteria:
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Patients undergoing 12-lead ECG and transthoracic echocardiography in routine clinical practice
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Hak Seung Lee, MD | Contact | +1-771-216-0764 | cardiolee@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Seung-Pyo Lee, MD, PhD | Seoul National University Hospital | Principal Investigator |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35328207 | Background | Kwon JM, Jo YY, Lee SY, Kang S, Lim SY, Lee MS, Kim KH. Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG. Diagnostics (Basel). 2022 Mar 8;12(3):654. doi: 10.3390/diagnostics12030654. | |
| 31074221 | Result | Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, Song PS, Park J, Choi RK, Oh BH. Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification. Korean Circ J. 2019 Jul;49(7):629-639. doi: 10.4070/kcj.2018.0446. Epub 2019 Mar 21. |
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| ID | Term |
|---|---|
| D018487 | Ventricular Dysfunction, Left |
| ID | Term |
|---|---|
| D018754 | Ventricular Dysfunction |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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| ID | Term |
|---|---|
| D004452 | Echocardiography |
| ID | Term |
|---|---|
| D057791 | Cardiac Imaging Techniques |
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
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| Through study completion, an average of 1 year |
| Positive predictive value | Positive predictive value in the recruited patient population | Through study completion, an average of 1 year |
| Negative predictive value | Negative predictive value in the recruited patient population | Through study completion, an average of 1 year |
| D014463 | Ultrasonography |
| D006334 | Heart Function Tests |
| D003935 | Diagnostic Techniques, Cardiovascular |