UltrasoundNCT06355557
Human vs Machine: a RCT Comparing Traditional In-person Instruction, AI Versus VR for Learning Basic CCE
A study testing AI enabled ultrasound system for self-directed learning, Simulator for self-directed learning and traditional with human instructors in people with Ultrasound, run by Tan Tock Seng Hospital at 1 site in Singapore.
Trial 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. Read more about this study
- Who is it for?
- Ages 21 years and olderHealthy volunteers can take part
- Conditions the study is for
- Ultrasound
- What is being tested?
- AI enabled ultrasound system for self-directed learning, Simulator for self-directed learning and traditional with human instructors
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 66 participants
- Where
- 1 siteSingapore
- Study dates
- 4 Apr 2024to 31 Jan 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Medical students will have limited clinical exposure to critical care echocardiography
- above the age of 21 years
Who cannot join
- prior attendance of a critical care echocardiography courses or
- refusal to participate in the study or complete both hands on sessions
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Human vs Machine: a Randomised Controlled Trial Comparing Traditional In-person Instruction, Artificial Intelligence Versus Virtual Reality for Learning Basic Critical Care Echocardiography
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.
Read the 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
- Being tested
- AI enabled ultrasound system for self-directed learning, Simulator for self-directed learning, traditional with human instructors
- Keywords
- artificial intelligence, echocardiography, POCUS
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI enabled ultrasound system for self-directed learningOther
Also called AI-enabled ultrasound system (Kosmos(TM))
use of the AI enabled ultrasound system for self-directed learning
Simulator for self-directed learningOther
use of the simulator for self-directed learning
traditional with human instructorsOther
Medical students who are randomised to this arm
| Study group | Role | What they receive |
|---|---|---|
| 22 medical students (AI) | Active comparator | medical students randomised to this arm Receives: Other: AI enabled ultrasound system for self-directed learning |
| 22 medical students (Simulator) | Active comparator | medical students randomised to this arm Receives: Other: Simulator for self-directed learning |
| 22 medical students (control) | Active comparator | medical students randomised to this arm Receives: Other: traditional with human instructors |
What is being measured
The primary outcome is the main question the study is designed to answer.
Improvement in image acquisition and structure identification at the end of 3 months.
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
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Apr 2024
- Study start4 Apr 2024
- Primary completion31 Dec 2024
- Study completion31 Jan 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for UltrasoundStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- 3-arm prospective randomised controlled trial.
- Masking
- Double blind
- Who is masked
- Investigator, Outcomes assessor
- Enrolment
- 66 participants (actual)
Sponsor and investigators
- Lead sponsor
- Tan Tock Seng Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Lau Yie Hui, Tan Tock Seng Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06355557
- Sponsor's protocol ID
- DSRB 2023/00640
- Status last verified
- Apr 2024
- First submitted
- 4 Apr 2024
- Last updated
- 1 Oct 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: local PDPA and data sharing rules
Publications
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.
PubMed 41269495
Study at a glance
From the registry record, last updated by the sponsor on 1 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Human vs Machine: a Randomised Controlled Trial Comparing Traditional In-person Instruction, Artificial Intelligence Versus Virtual Reality for Learning Basic Critical Care Echocardiography
- ClinicalTrials.gov ID
- NCT06355557
- Acronym / sponsor ID
- DSRB 2023/00640
- Conditions
- Ultrasound
- Keywords
- artificial intelligence, echocardiography, POCUS
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 66 participants (actual)
- Lead sponsor
- Tan Tock Seng HospitalUniversity, hospital or other
- Study start
- 4 Apr 2024 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- 31 Jan 2025 (actual)
- First posted
- 9 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (investigator, outcomes assessor)
- Number of arms
- 3
- Model description
- 3-arm prospective randomised controlled trial.
| Arm | Type | Receives |
|---|---|---|
| 22 medical students (AI) | Active comparator | Other: AI enabled ultrasound system for self-directed learningmedical students randomised to this arm |
| 22 medical students (Simulator) | Active comparator | Other: Simulator for self-directed learningmedical students randomised to this arm |
| 22 medical students (control) | Active comparator | Other: traditional with human instructorsmedical students randomised to this arm |
Who may qualify
- Ages
- 21 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Medical students will have limited clinical exposure to critical care echocardiography
- above the age of 21 years
Key exclusion criteria (2 listed)
- prior attendance of a critical care echocardiography courses or
- refusal to participate in the study or complete both hands on sessions
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI enabled ultrasound system for self-directed learningAlso: AI-enabled ultrasound system (Kosmos(TM))use of the AI enabled ultrasound system for self-directed learning | Other | 22 medical students (AI) |
| Simulator for self-directed learninguse of the simulator for self-directed learning | Other | 22 medical students (Simulator) |
| traditional with human instructorsMedical students who are randomised to this arm | Other | 22 medical students (control) |
Primary outcome
Improvement in image acquisition and structure identification at the end of 3 months.
Time frame: 3 months
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Oct 2026.
- Derived from this study: PubMed 41269495Lau 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.
- Individual participant dataSharing planned: No. local PDPA and data sharing rules
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI enabled ultrasound system for self-directed learning, Simulator for self-directed learning and traditional with human instructors in people with Ultrasound, run by Tan Tock Seng Hospital at 1 site in Singapore.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 1 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Singapore. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Improvement in image acquisition and structure identification at the end of 3 months. (time frame: 3 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06355557, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Oct 2026.