Ignite Creation Date:
2025-12-24 @ 7:06 PM
Ignite Modification Date:
2025-12-29 @ 8:00 AM
Study NCT ID:
NCT06355557
Status:
RECRUITING
Last Update Posted:
2024-04-09
First Post:
2024-04-04
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Human vs Machine: a RCT Comparing Traditional In-person Instruction, AI Versus VR for Learning Basic CCE
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'DOUBLE', 'whoMasked': ['INVESTIGATOR', 'OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'OTHER', 'interventionModel': 'PARALLEL', 'interventionModelDescription': '3-arm prospective randomised controlled trial.'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 66}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-04-04', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-04', 'completionDateStruct': {'date': '2025-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-04-08', 'studyFirstSubmitDate': '2024-04-04', 'studyFirstSubmitQcDate': '2024-04-08', 'lastUpdatePostDateStruct': {'date': '2024-04-09', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-04-09', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Improvement in image acquisition and structure identification at the end of 3 months.', 'timeFrame': '3 months', 'description': 'The images acquired during that timeframe will be scored using the validated Rapid Assessment of Competency in Echocardiography Scale. The experienced CCE trainer who will score the subject will be blinded to which arm of the study the subject is in'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['artificial intelligence, echocardiography, POCUS'], 'conditions': ['Ultrasound']}, 'referencesModule': {'references': [{'pmid': '41269495', 'type': 'DERIVED', 'citation': 'Lau YH, Acharyya S, Wee CWL, Xu H, Saclolo RP, Cao K, Fong WK. Effectiveness of traditional, artificial intelligence-assisted, and virtual reality training modalities for focused cardiac ultrasound skill acquisition: a randomised controlled study. Ultrasound J. 2025 Nov 21;17(1):61. doi: 10.1186/s13089-025-00469-7.'}]}, 'descriptionModule': {'briefSummary': 'The aim of the study is to investigate if hands-on training for basic CCE with virtual reality simulators or guided by artificial intelligence is non-inferior to training by an experienced instructor.', 'detailedDescription': 'Basic (Level 1) Critical care echocardiography (CCE) involves using an ultrasound device to qualitatively assess the heart at the bedside. It is increasingly being used at the bedside for diagnostics and screening of key differential diagnoses. Increasingly, CCE is being taught to more medical staff from many fields in medicine, including emergency medicine, anaesthesiology, intensive care medicine and even family medicine. There is a wealth of learning resources online but access to direct supervision by trainers and in-person courses is can be limited and costly. At the time of the study, one local medical school incorporated a lecture there is no credentialling pathway within local medical schools or institution. There has been increasing use of machine learning in medical imaging and deep learning algorithms are now able to guide image acquisition and allow novices with minimal training in echocardiography to obtain diagnostic-quality images. Artificial intelligence (AI) in echocardiography may improve image by novices. Ultrasound hardware that implement machine learning software in real-time can help with structure detection and identification, but more studies are needed to determine the extent that AI impacts learning.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '21 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Medical students will have limited clinical exposure to critical care echocardiography\n* above the age of 21 years\n\nExclusion Criteria:\n\n* prior attendance of a critical care echocardiography courses or\n* refusal to participate in the study or complete both hands on sessions'}, 'identificationModule': {'nctId': 'NCT06355557', 'briefTitle': 'Human vs Machine: a RCT Comparing Traditional In-person Instruction, AI Versus VR for Learning Basic CCE', 'organization': {'class': 'OTHER', 'fullName': 'Tan Tock Seng Hospital'}, 'officialTitle': 'Human vs Machine: a Randomised Controlled Trial Comparing Traditional In-person Instruction, Artificial Intelligence Versus Virtual Reality for Learning Basic Critical Care Echocardiography', 'orgStudyIdInfo': {'id': 'DSRB 2023/00640'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'ACTIVE_COMPARATOR', 'label': '22 medical students (AI)', 'description': 'medical students randomised to this arm', 'interventionNames': ['Other: AI enabled ultrasound system for self-directed learning']}, {'type': 'ACTIVE_COMPARATOR', 'label': '22 medical students (Simulator)', 'description': 'medical students randomised to this arm', 'interventionNames': ['Other: Simulator for self-directed learning']}, {'type': 'ACTIVE_COMPARATOR', 'label': '22 medical students (control)', 'description': 'medical students randomised to this arm', 'interventionNames': ['Other: traditional with human instructors']}], 'interventions': [{'name': 'AI enabled ultrasound system for self-directed learning', 'type': 'OTHER', 'otherNames': ['AI-enabled ultrasound system (Kosmos(TM))'], 'description': 'use of the AI enabled ultrasound system for self-directed learning', 'armGroupLabels': ['22 medical students (AI)']}, {'name': 'Simulator for self-directed learning', 'type': 'OTHER', 'description': 'use of the simulator for self-directed learning', 'armGroupLabels': ['22 medical students (Simulator)']}, {'name': 'traditional with human instructors', 'type': 'OTHER', 'description': 'Medical students who are randomised to this arm', 'armGroupLabels': ['22 medical students (control)']}]}, 'contactsLocationsModule': {'locations': [{'zip': '319581', 'city': 'Singapore', 'status': 'RECRUITING', 'country': 'Singapore', 'contacts': [{'name': 'Yie H Lau', 'role': 'CONTACT', 'email': 'yie_hui_lau@ttsh.com.sg', 'phone': '6563577771'}, {'name': 'Yie H Lau', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Tan Tock Seng Hospital', 'geoPoint': {'lat': 1.28967, 'lon': 103.85007}}], 'centralContacts': [{'name': 'Yie H Lau', 'role': 'CONTACT', 'email': 'yie_hui_lau@ttsh.com.sg', 'phone': '6563577771'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'local PDPA and data sharing rules'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Tan Tock Seng Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Senior Consultant', 'investigatorFullName': 'Lau Yie Hui', 'investigatorAffiliation': 'Tan Tock Seng Hospital'}}}}