[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Idoven 1903 S.L.\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":160},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,5,0,[8,44,70,94,128],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":13,"acronym":14,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":26,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":33,"lastUpdatePostDateStruct":34,"startDateStruct":37,"completionDateStruct":39,"leadSponsor":41,"locationsCount":5},"100618586","registry-study-for-the-evaluation-of-high-risk-cardiac-patients-by-willem-ai-based-ecg-platform-100618586",false,"NCT07333547","Registry Study for the Evaluation of High-risk Cardiac Patients by WILLEM AI-based ECG Platform","WILLEMRegistry","Inclusion Criteria:\n\n* EC\u002FIRB approval of ICF waiver prior to recruitment; otherwise, signed informed consent form by subject and investigator\n* Age \\> 18 years-old, with no upper limit\n* Subjects undergoing standard of care electrocardiogram (ECG) of any duration from any hardware device\n* All available, but at least one, legible ECG tracings in raw data format (e.g. DICOM, XML, EDF, JSON, HL7, SCP, WFDB, CSV, etc.)\n* Available subject clinical data associated with the ECG\n* For 12-lead ECGs, a minimum length of 10 seconds at a minimum sample frequency of 250 Hz\n* For ECGs from Holters, wearables, patches, insertable cardiac monitors, telemetries, etc., a minimum length of 30 seconds at a minimum sample frequency of 200 Hz with a lead I \u002F II or its MCL-DII lead approximation\n* For prospective eligibility only:\n* Signed informed consent form, unless previously waived by the EC\u002FIRB\n* Site technical viability for ECG and subject clinical data transfer (e.g. end-to-end integration following interoperability standards such as FHIR, HL7 or DICOM)\n\nExclusion Criteria:\n\n* Unavailable or suboptimal quality of the raw data from the ECG signal\n* Age \\\u003C 18 years-old",true,"ALL","18 Years",{"count":20,"type":21},200000,"ESTIMATED","OBSERVATIONAL","The WILLEM Registry is a large-scale, single-group, observational, registry study to collect continuous clinical evidence of Willem in real-world settings. Cardiovascular diseases are a major problem for public health and healthcare systems. Electrocardiograms (ECGs) are simple tests which increase diagnostic performance and early detection of cardiovascular diseases. However, its interpretation is complex, time consuming for cardiology experts, and entails high costs for healthcare systems. Willem allows AI-based automatic interpretation and its performance has been examined in previous clinical trials, but additional clinical evidence is needed for its integration in real-world clinical settings. This study will collect clinical evidence of Willem performance to detect cardiac abnormalities in ECGs from high-risk cardiac patients admitted to cardiovascular units.",[25],"High-risk Cardiac Patients",[27,28,29,30,31],"artificial intelligence","electrocardiogram","deep learning","cardiac disease","registry","RECRUITING","2026-07-01",{"date":35,"type":36},"2026-07-02","ACTUAL",{"date":38,"type":36},"2026-02-03",{"date":40,"type":21},"2036-01",{"name":42,"class":43},"Idoven 1903 S.L.","INDUSTRY",{"id":45,"slug":46,"hasResults":11,"nctId":47,"briefTitle":48,"officialTitle":49,"acronym":50,"eligibilityCriteria":51,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":52,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":54,"conditions":55,"keywords":57,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":61,"lastUpdatePostDateStruct":62,"startDateStruct":64,"completionDateStruct":66,"leadSponsor":68,"locationsCount":69},"100591302","multicenter-study-for-the-validation-of-an-ai-based-ecg-platform-for-early-cardiac-amyloidosis-diagnosis-100591302","NCT06978660","Multicenter Study for the Validation of an AI-based ECG Platform for Early Cardiac Amyloidosis Diagnosis","Multicenter Study for the Validation of an AI-based ECG Platform for Early Cardiac Amyloidosis Diagnosis (CONCERTO)","CONCERTO","Inclusion Criteria:\n\n* Subjects ≥ 18 years old\n* Subjects with 12-leads ECG records with a 10 seconds minimum length on digital format\n\nExclusion Criteria:\n\n* Patients with paced rhythm on the ECG.",{"count":53,"type":21},2200,"CONCERTO is a retrospective, observational, multicentric and single-arm study to perform an external validation of the cloud-based and AI-powered electrocardiogram (ECG) analysis platform, named Willem™, to detect Transthyretin cardiac amyloidosis (ATTR-CA). Thus, this study will assess Willem™ ability to distinguish between truly diagnosed ATTR-CA patients and confirmed non-ATTR-CA patients from ECG data.",[56],"Transthyretin Cardiac Amyloidosis",[58,59,60],"Transthyretin cardiac amyloidosis","ATTR","AI-based ECG Analysis","2026-06-22",{"date":63,"type":36},"2026-06-23",{"date":65,"type":36},"2025-05-27",{"date":67,"type":21},"2026-12-31",{"name":42,"class":43},8,{"id":71,"slug":72,"hasResults":11,"nctId":73,"briefTitle":74,"officialTitle":75,"acronym":76,"eligibilityCriteria":77,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":78,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":80,"conditions":81,"keywords":83,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":85,"lastUpdatePostDateStruct":86,"startDateStruct":88,"completionDateStruct":90,"leadSponsor":92,"locationsCount":93},"100629847","multicenter-study-for-the-validation-of-willem-ai-aortic-stenosis-early-diagnosis-with-ai-electrocardiogram-study-100629847","NCT07479992","Multicenter Study for the Validation of Willem AI: Aortic StenoSis Early Diagnosis With AI-electrocardiogram Study","Multicenter Study for the Validation of Willem AI: Aortic StenoSis Early Diagnosis With AI-electrocardiogram (Willem AoS-SEDAI) Study","AoS-SEDAI","Inclusion Criteria:\n\n* Age ≥ 18 years;\n* All available 12-lead ECG with a 10 seconds minimum length on raw data digital format will be included\n* Available clinical data corresponding to each ECG to confirm patient demographics and Aortic Stenosis diagnosis\n* Available transthoracic echocardiogram (TTE) within +\u002F- 90 days of each ECG recording\n\nNo exclusion criteria are defined for this study.",{"count":79,"type":21},5000,"AoS-SEDAI study is an observational, multicenter, retrospective and prospective clinical study.\n\nThis study aims to assess Willem Artificial Intelligence (AI) ability to distinguish between aortic stenosis (AS) and non-AS patients from 12-lead electrocardiogram (ECG) data.",[82],"Aortic Stenosis",[27,28,29,30,84],"aortic stenosis","2026-05-08",{"date":87,"type":36},"2026-05-12",{"date":89,"type":36},"2026-04-28",{"date":91,"type":21},"2026-07",{"name":42,"class":43},2,{"id":95,"slug":96,"hasResults":11,"nctId":97,"briefTitle":98,"officialTitle":99,"acronym":100,"eligibilityCriteria":101,"healthyVolunteers":11,"sex":17,"minAge":102,"maxAge":4,"enrollmentInfo":103,"targetDuration":4,"studyType":105,"phases":106,"briefSummary":108,"conditions":109,"keywords":112,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":120,"lastUpdatePostDateStruct":121,"startDateStruct":123,"completionDateStruct":125,"leadSponsor":127,"locationsCount":5},"100601648","determining-efficacy-of-an-artificial-intelligence-based-system-for-heart-failure-detection-through-interpretation-of-electrocardiograms-decision-100601648","NCT07113223","Determining Efficacy of an Artificial Intelligence-based System for Heart Failure Detection Through Interpretation of Electrocardiograms (DECISION)","Determining Efficacy of an Artificial Intelligence-based System for Heart Failure Detection Through Interpretation of Electrocardiograms: a Pragmatic Randomized Clinical Trial (DECISION)","DECISION","Inclusion Criteria:\n\n* Patients with Suspected HF (Group S):\n\n  * Able to understand and accept the study constraints and to provide informed consent (either themselves or a legal representative).\n  * Age over 65 years (i.e., 65 included).\n  * Presence of symptoms and\u002For signs typical of Heart Failure (defined by the European Society of Cardiology, ESC), including breathlessness (during activity or at rest, lying down, waking up at night needing to catch their breath), fatigue, swollen ankles\u002Flegs, and\u002For palpitations.\n* Patients at Risk of Heart Failure due to the presence of cardiovascular (Group R):\n\n  * Able to understand and accept the study constraints and to provide informed consent (either themselves or a legal representative).\n  * Age over 65 years (i.e., 65 included).\n  * Absence of symptoms and\u002For signs typical of Heart Failure (defined by the ESC), including breathlessness (during activity or at rest, lying down, waking up at night needing to catch their breath), fatigue, swollen ankles\u002Flegs, and\u002For palpitations.\n  * Presence of at least 1 ACC\u002FAHA Heart Failure risk factor, including hypertension, cardiovascular disease (atrial fibrillation, coronary heart disease or stroke), diabetes, obesity, exposure to cardiotoxic agents, genetic variant for cardiomyopathy, or family history of cardiomyopathy that requires an ECG test for any reason in a primary care center or with an indication of a regular health examination where an ECG is included.\n\nExclusion Criteria:\n\n* Unwillingness or inability to sign the written informed consent.\n* Previous Heart Failure diagnosis.\n* Unavailability or suboptimal quality ECG.","65 Years",{"count":104,"type":21},1968,"INTERVENTIONAL",[107],"NA","The DECISION trial aims to evaluate the efficacy of an artificial intelligence (AI)-powered system, Willem™, for improving the detection of heart failure (HF) in primary care settings by interpreting electrocardiograms (ECGs). The study seeks to answer whether AI-assisted ECG interpretation enhances diagnostic accuracy and clinical outcomes compared to standard ECG evaluation in patients with suspected HF or those at high risk.\n\nThis multicenter, pragmatic, randomized clinical trial involves two groups: patients receiving AI-assisted ECG analysis and those undergoing standard ECG evaluation. The study's primary analysis will compare the diagnostic performance of AI-assisted ECG versus standard ECG using sensitivity, specificity, and predictive value metrics. Secondary analyses will evaluate healthcare resource utilization, clinical outcomes, and usability feedback from healthcare providers. Results will inform the potential integration of AI-assisted ECG in routine primary care workflows for earlier HF detection and better resource allocation.",[110,111],"Heart Failure","Cardiovascular Risk Factors",[110,113,114,115,116,117,118,119],"Artificial Intelligence","Electrocardiogram (ECG)","Primary Care","Diagnostic Tools","Cardiology","Electrocardiography","Risk Stratification","2026-03-30",{"date":122,"type":36},"2026-03-31",{"date":124,"type":36},"2025-07-23",{"date":126,"type":21},"2026-09",{"name":42,"class":43},{"id":129,"slug":130,"hasResults":11,"nctId":131,"briefTitle":132,"officialTitle":133,"acronym":134,"eligibilityCriteria":135,"healthyVolunteers":16,"sex":17,"minAge":136,"maxAge":4,"enrollmentInfo":137,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":139,"conditions":140,"keywords":145,"overallStatus":32,"whyStopped":4,"lastUpdateSubmitDate":120,"lastUpdatePostDateStruct":152,"startDateStruct":154,"completionDateStruct":156,"leadSponsor":158,"locationsCount":159},"100507694","ai-powered-ecg-analysis-using-willem-software-in-high-risk-cardiac-patients-willem-100507694","NCT05890716","AI-powered ECG Analysis Using Willem™ Software in High-risk Cardiac Patients (WILLEM)","Evaluation of Electrocardiographic Data From High-risk Cardiac Patients Using Willem™ Cardiologist-level Artificial Intelligence Software. WILLEM Trial.","WILLEM","Inclusion Criteria:\n\n* Patient presenting relevant cardiac arrhythmias and cardiac patterns (including supraventricular tachycardias, abnormal ECG patterns, ventricular tachycardias, ventricular fibrillation, pulseless electrical activity or asystole among others) that have been recorded with at least one short-term ECG medical device according to guidelines with ≥1 signal-channel.\n* Patient with suspected or diagnosed acute\u002Fchronic cardiac diseases (including patients with heart failure, patients with history of cardiac arrhythmias, patients with probable coronary artery diseases, patients with cardiomyopathies, patients with pacemakers or implantable cardioverter-defibrillators (ICD), patients with indication of pacemaker or ICD in current or short-term phase, patients participating in other interventional clinical investigation, patients with hemodynamic instability or acute coronary syndromes, pregnant patients, patients with cancer and chemotherapy, patients with life-expectancy lower than 24 months, patients with in or out-of-hospital cardiac arrest with ventricular fibrillation as first documented rhythm).\n* At least one ECG tracing that can be exported in raw data.\n* Signed informed consent. Patients unable to consent, it will be requested to an authorized relative.\n\nExclusion Criteria:\n\n* Unwillingness or inability to sign study written informed consent.\n* Unavailable or suboptimal quality of the electrocardiographic signal in raw data.","4 Years",{"count":138,"type":21},5342,"WILLEM is a multi-center, prospective and retrospective cohort study.\n\nThe study will assess the performance of a cloud-based and AI-powered ECG analysis platform, named Willem™, developed to detect arrhythmias and other abnormal cardiac patterns. The main questions it aims to answer are:\n\n1. A new AI-powered ECG analysis platform can automatice the classification and prediction of cardiac arrhythmic episodes at a cardiologist level.\n2. This AI-powered ECG analysis can delay or even avoid harmful therapies and severe cardiac adverse events such as sudden death.\n\nThe prerequisites for inclusion of patients will be the availability of at least one ECG record in raw data, along with patient clinical data and evolution data after more than 1-year follow-up.\n\nCardiac electrical signals from multiple medical devices will be collected by cardiology experts after obtaining the informed consent. Every cardiac electrical signal from every subject will be reviewed by a board-certified cardiologist to label the arrhythmias and patterns recorded in those tracings. In order to obtain tracings of relevant information, \\>95% of the subjects enrolled will have rhythm disorders or abnormal ECG's patterns at the time of enrollment.",[141,142,143,144],"Cardiomyopathies","Cardiac Arrest","Cardiac Arrhythmias","Sudden Cardiac Death",[146,147,148,149,150,151],"Artificial intelligence","Cardiac arrhythmias","Heart disease","Cardiac electrical signals","Electrocardiogram","Electrogram",{"date":153,"type":36},"2026-04-03",{"date":155,"type":36},"2023-04-04",{"date":157,"type":21},"2026-11",{"name":42,"class":43},14,""]