Ignite Creation Date:
2025-12-24 @ 2:16 PM
Ignite Modification Date:
2025-12-29 @ 8:18 PM
Study NCT ID:
NCT06314295
Status:
RECRUITING
Last Update Posted:
2025-11-21
First Post:
2024-03-11
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003324', 'term': 'Coronary Artery Disease'}], 'ancestors': [{'id': 'D003327', 'term': 'Coronary Disease'}, {'id': 'D017202', 'term': 'Myocardial Ischemia'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D001161', 'term': 'Arteriosclerosis'}, {'id': 'D001157', 'term': 'Arterial Occlusive Diseases'}, {'id': 'D014652', 'term': 'Vascular Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-03-11', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2026-05-11', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-18', 'studyFirstSubmitDate': '2024-03-11', 'studyFirstSubmitQcDate': '2024-03-11', 'lastUpdatePostDateStruct': {'date': '2025-11-21', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2024-03-18', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-03-11', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Different coronary angiography results', 'timeFrame': 'Coronary angiography examination within 2-3 days after admission', 'description': 'The degree of coronary artery stenosis'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Coronary Artery Disease']}, 'descriptionModule': {'briefSummary': 'The incidence rate and mortality of coronary artery disease are increasing year by year. Exploring non-invasive, accurate, and widely applicable methods to screen and diagnosis is of great significance. New ultrasound techniques, such as non-invasive myocardial work, have been proven to be superior to traditional ultrasound techniques in screening and diagnosis. However, diagnostic analysis based on ultrasound video images is time-consuming and subjective. The progress of artificial intelligence technology in fully automated quantitative evaluation of video images provides the possibility for computer-aided design screening and diagnosis. At present, the application of artificial intelligence in computer-aided design is a cutting-edge issue in the field of cardiovascular disease research. The application of artificial intelligence technology in the construction of computer-aided diagnostic models based on ultrasound video images is still in its early stages.', 'detailedDescription': '1\\) Clarify the value of new cardiac ultrasound techniques indicators in coronary artery disease diagnosis; 2) To achieve classification and detection of cardiac ultrasound sections; Implementing automatic segmentation and recognition of the left ventricular cavity, left ventricular myocardium, and left atrial wall contours through the CLAS model; Using the another model to achieve heart motion tracking and synthesizing velocity vector maps of the heart flow field. 3) Verify and optimize the coronary artery disease fully automated artificial intelligence diagnostic model mentioned above.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '90 Years', 'minimumAge': '18 Years', 'samplingMethod': 'PROBABILITY_SAMPLE', 'studyPopulation': 'Patients with suspected coronary artery disease who plan to undergo coronary angiography', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients with suspected coronary artery disease\n* Patients plan to undergo coronary angiography\n\nExclusion Criteria:\n\n* Patients with aortic valve stenosis\n* Patients with aortic valve replacement surgery\n* Patients with hypertrophic cardiomyopathy\n* Patients with severe heart valve disease\n* Patients with severe arrhythmia\n* Patients with severe cardiomyopathy\n* Patients with severe congenital heart disease\n* The quality of ultrasound images is poor'}, 'identificationModule': {'nctId': 'NCT06314295', 'briefTitle': 'Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods', 'organization': {'class': 'OTHER_GOV', 'fullName': 'Beijing Hospital'}, 'officialTitle': 'Automated Echocardiographic Detection of Coronary Artery Disease Using Artificial Intelligence Methods', 'orgStudyIdInfo': {'id': 'BeijingH-WF'}}, 'contactsLocationsModule': {'locations': [{'city': 'Beijing', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Fang Wang, Dr', 'role': 'CONTACT', 'email': 'bjh_wangfang@163.com'}], 'facility': 'Beijing Hospital', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Beijing Hospital', 'class': 'OTHER_GOV'}, 'responsibleParty': {'type': 'SPONSOR'}}}}