[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100659002":3},{"organization":4,"outcomesModule":7,"designInfo":16,"detailedDescription":11,"studyPopulation":19,"armGroups":20,"interventions":26,"overallOfficials":11,"centralContacts":32,"locations":38,"responsibleParty":52,"collaborators":11,"id":56,"slug":57,"hasResults":58,"nctId":59,"briefTitle":60,"officialTitle":61,"acronym":62,"eligibilityCriteria":63,"healthyVolunteers":58,"sex":64,"minAge":65,"maxAge":11,"enrollmentInfo":66,"targetDuration":11,"studyType":69,"phases":11,"briefSummary":70,"conditions":71,"keywords":77,"overallStatus":40,"whyStopped":11,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":84,"startDateStruct":87,"completionDateStruct":89,"leadSponsor":91,"locationsCount":92},{"fullName":5,"class":6},"Inha University Hospital","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":11},[9],{"measure":10,"description":11,"timeFrame":12},"Diagnostic Performance (AUC-ROC) of AI-ECG Models",null,"Baseline",[14],{"measure":15,"description":11,"timeFrame":12},"Localization Accuracy of Tachycardia Origin Sites",{"allocation":11,"interventionModel":11,"interventionModelDescription":11,"primaryPurpose":11,"observationalModel":17,"timePerspective":18,"maskingInfo":11},"COHORT","RETROSPECTIVE","Retrospective cohort of patients diagnosed with tachycardias who underwent electrophysiological studies and catheter ablation at participating institutions\\[cite: 1\\].",[21],{"label":22,"type":11,"description":23,"interventionNames":24},"Cardiovascular Disease and Tachycardia Cohort","Patients aged 19 years or older diagnosed with tachycardias (including PSVT, WPW syndrome, PVC, or VT) who underwent an electrophysiological study (EPS) and radiofrequency catheter ablation (RFCA), with extractable 12-lead ECG text data\\[cite: 1\\].",[25],"Diagnostic Test: AI-based ECG Diagnostic and Localization Algorithm",[27],{"type":28,"name":29,"description":30,"armGroupLabels":31,"otherNames":11},"DIAGNOSTIC_TEST","AI-based ECG Diagnostic and Localization Algorithm","Application of a deep learning-based 12-lead electrocardiogram (ECG) algorithm to analyze raw text data for the differential diagnosis and localization of tachycardias, including PSVT, WPW syndrome, PVC, and VT\\[cite: 1\\].",[22],[33],{"name":34,"role":35,"phone":36,"phoneExt":11,"email":37},"Yongsoo Baek, MD, PhD","CONTACT","+82-32-890-2200","existsoo@inha.ac.kr",[39],{"facility":5,"status":40,"city":41,"state":41,"zip":42,"country":43,"countryCode":11,"cosmosGeoPoint":44,"geoPoint":49,"contacts":50},"RECRUITING","Incheon","22332","South Korea",{"type":45,"coordinates":46},"Point",[47,48],126.70515,37.45646,{"lat":48,"lon":47},[51],{"name":34,"role":35,"phone":36,"phoneExt":11,"email":37},{"type":53,"investigatorFullName":54,"investigatorTitle":55,"investigatorAffiliation":5,"oldNameTitle":11,"oldOrganization":11},"PRINCIPAL_INVESTIGATOR","Yong-Soo Baek","Professor","100659002","ai-based-differential-diagnosis-and-localization-of-tachycardia-using-ecg-multicenter-registry-ep-ai-assist-100659002",false,"NCT07859020","AI-Based Differential Diagnosis and Localization of Tachycardia Using ECG: Multicenter Registry (EP-AI ASSIST)","AI-based Differential Diagnosis and Localization of Tachycardia Using ECG: Multicenter Registry (EP-AI ASSIST)","EP-AI ASSIST","Inclusion Criteria:\n\n* Patients aged 19 years or older diagnosed with tachycardias (including PSVT, WPW syndrome, PVC, or VT) who have undergone an electrophysiological study (EPS) and radiofrequency catheter ablation (RFCA).\n* Patients with 12-lead electrocardiogram (ECG) records extractable as text (XML) files.\n\nExclusion Criteria:\n\n\\- Patients deemed inappropriate as study subjects by the investigator's judgment based on data quality or missing medical records.","ALL","19 Years",{"count":67,"type":68},10000,"ESTIMATED","OBSERVATIONAL","This multicenter retrospective study aims to develop, validate, and clinically apply an artificial intelligence (AI)-powered electrocardiography (ECG) algorithm for the diagnosis and prognostic prediction of cardiovascular diseases, including coronary artery disease and heart failure. By utilizing encrypted raw 12-lead ECG text data and deep neural network models, the study evaluates the effectiveness of AI-enhanced ECG in detecting cardiac abnormalities and predicting clinical outcomes.",[72,73,74,75,76],"Tachycardia","Paroxysmal Supraventricular Tachycardia","Wolff-Parkinson-White Syndrome","Premature Ventricular Contractions","Ventricular Tachycardia",[78,79,80,81,82],"Artificial Intelligence","Electrocardiogram","Transformer","Catheter Ablation","Multicenter Study","2026-09-29",{"date":85,"type":86},"2026-10-05","ACTUAL",{"date":88,"type":86},"2023-12-01",{"date":90,"type":68},"2028-12-01",{"name":5,"class":6},1]