Human vs Machine: a RCT Comparing Traditional In-person Instruction, AI Versus VR for Learning Basic CCE

NCT ID: NCT06355557

Last Updated: 2024-04-09

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 66 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-04-04
• Study Completion Date: 2025-01-31

Brief Summary

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.

Detailed Description

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.

Conditions

Ultrasound

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: DOUBLE

Study Groups

22 medical students (AI)

medical students randomised to this arm

Group Type: ACTIVE_COMPARATOR

AI enabled ultrasound system for self-directed learning

Intervention Type: OTHER

use of the AI enabled ultrasound system for self-directed learning

22 medical students (Simulator)

medical students randomised to this arm

Group Type: ACTIVE_COMPARATOR

Simulator for self-directed learning

Intervention Type: OTHER

use of the simulator for self-directed learning

22 medical students (control)

medical students randomised to this arm

Group Type: ACTIVE_COMPARATOR

traditional with human instructors

Intervention Type: OTHER

Medical students who are randomised to this arm

Interventions

AI enabled ultrasound system for self-directed learning

use of the AI enabled ultrasound system for self-directed learning

Intervention Type: OTHER

Simulator for self-directed learning

use of the simulator for self-directed learning

Intervention Type: OTHER

traditional with human instructors

Medical students who are randomised to this arm

Intervention Type: OTHER

Other Intervention Names

AI-enabled ultrasound system (Kosmos(TM))

Eligibility Criteria

Inclusion Criteria

• Medical students will have limited clinical exposure to critical care echocardiography
• above the age of 21 years

Exclusion Criteria

• prior attendance of a critical care echocardiography courses or
• refusal to participate in the study or complete both hands on sessions

• Minimum Eligible Age: 21 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Tan Tock Seng Hospital

OTHER

Sponsor Role: lead

Responsible Party

Lau Yie Hui

Senior Consultant

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Tan Tock Seng Hospital

Singapore, , Singapore

Site Status: RECRUITING

Countries

Singapore

Central Contacts

Yie H Lau

Role: CONTACT

yie_hui_lau@ttsh.com.sg

6563577771

Facility Contacts

Yie H Lau

Role: primary

yie_hui_lau@ttsh.com.sg

6563577771

Other Identifiers

DSRB 2023/00640

Identifier Type: -

Identifier Source: org_study_id