Fetal Weightscan-AIdNCT06232187
AI Support in Novice's Decision-making for Ultrasound Fetal Weight Estimation
A study testing Artificial Intelligence feedback for ultrasound EFW standard plane images in people with Fetal Weight and Ultrasound, run by Copenhagen Academy for Medical Education and Simulation at 1 site in Denmark.
Trial summary
The SCAN-AID study is a prospective, randomized, controlled, and unblinded study that compares the performance of novices in ultrasound fetal weight estimation. The study evaluates the impact of two levels of AI support: a straightforward black box AI and a more detailed explainable AI. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Fetal Weight, Ultrasound
- What is being tested?
- Artificial Intelligence feedback for ultrasound EFW standard plane images
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 75 participants
- Where
- 1 siteDenmark
- Study dates
- 14 Feb 2024to 1 Sep 2024
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Medical students with no former fetal or abdominal ultrasound training.
- The participants will have to understand spoken and written Danish or English.
- The participants will have to understand spoken and written Danish or English.
- BMI < 30
- Gestational age: 28-42
Who cannot join
- Medical students who received formal fetal or abdominal training prior to the inclusion in this study.
- Pregnant women;
- Age > 40 years
- Fefal anomaly
- Oligohydramnion
- Gestational Diabetes, Diabetes type 1 or 2.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Scan-AId: Artificial Intelligence Support in Novice's Decision-making for Assessing Ultrasound Fetal Weight Estimation - A Randomized Trial
The SCAN-AID study is a prospective, randomized, controlled, and unblinded study that compares the performance of novices in ultrasound fetal weight estimation. The study evaluates the impact of two levels of AI support: a straightforward black box AI and a more detailed explainable AI.
Read the detailed description
The goal of this randomized controlled clinical trial is to learn which type of artificial intelligence (AI) effects the diagnostic accuracy of ultrasound estimation of fetal weight (EFW), when performed by novices, in this study represented by medical students.
The study's objectives are:
- Which type of artificial intelligence support system works for novices in improving the ultrasound fetal weight diagnostic accuracy?
- Does the artificial intelligence improve image quality, evaluate the cognitive load placed on participants when utilizing AI support, and is the AI system usable for novices?
Participants will be tasked with conducting an ultrasound Estimated Fetal Weight (EFW) using either a simple black box AI or a detailed explainable AI feedback system. The AI systems will assist participants in determining if they have captured the appropriate image for EFW. The outcomes will then be compared to those of a control group.
Ultrasound procedures will be performed on pregnant women with fetuses at a gestational age of 28-42 weeks, who have previously undergone an EFW by an expert sonographer or doctor at the clinic within 5 days days leading up to the examinationday. One participant of each randomization arm, will perfrom an EFW on the same pregnant woman.
- Conditions
- Fetal Weight, Ultrasound
- Being tested
- Artificial Intelligence feedback for ultrasound EFW standard plane images
- Keywords
- Artificial Intelligence, Fetal weight estimation, novices
- Medical subject headings
- Fetal Weight
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence feedback for ultrasound EFW standard plane imagesBehavioural
AI feedback in two levels, in aid of the participants, to obtain the right standardplane images used in fetal ultrasound EFW calculation.
| Study group | Role | What they receive |
|---|---|---|
| Feedback Group 1 (FG1) | Experimental | Participatns in FG1 will receive basic black box AI support, with simple explanation like "standard plane", "non standard plane" or "off plane". Receives: Behavioral: Artificial Intelligence feedback for ultrasound EFW standard plane images |
| Feedback Group 2 (FG2) | Experimental | Participants in FG2 will receive explainable AI support, with more elaborate description of the anatomical structures and segmentation of the anatomy. Receives: Behavioral: Artificial Intelligence feedback for ultrasound EFW standard plane images |
| Control group (CG) | No intervention | Participants in the CG will have a standard plane poster to help guide them to the EFW ultrasound standard plane images. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy
The accuracy in each group was defined as the percentage difference between estimated fetal weight and the sonographer expert EFW
Measured: 15 minutes
Secondary outcomes (1)
Image Quality
Salomon criteria score is used to rate the image quality. Points are given depending on the number of landmarks present, quality of the image optimization and caliper.placements.
Minimum: 1 Maximum: 18. A higher score indicates a better image quality.
Measured: 5 minutes pr. participant
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jan 2024
- Study start14 Feb 2024
- Primary completion30 Jun 2024 (estimated)
- Study completion1 Sep 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (16)Papers the sponsor has linked to this study.
- Other trials for Fetal WeightStudies 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
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- The participants are allocated to one of three groups: control group, feedback group 1 with black box AI or feedback group 2 with explainable AI feedback.
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 75 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Copenhagen Academy for Medical Education and Simulation
- Sponsor type
- University, hospital or other
- Collaborators
- Slagelse Hospital, Technical University of Denmark
- Responsible party
- Principal investigator, Mary Le Ngo, Copenhagen Academy for Medical Education and Simulation
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06232187
- Sponsor's protocol ID
- F-24001576
- Acronym
- scan-AId
- Status last verified
- Apr 2024
- First submitted
- 22 Jan 2024
- Last updated
- 10 May 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Andreasen LA, Tabor A, Norgaard LN, Rode L, Gerds TA, Tolsgaard MG. Detection of growth-restricted fetuses during pregnancy is associated with fewer intrauterine deaths but increased adverse childhood outcomes: an observational study. BJOG. 2021 Jan;128(1):77-85. doi: 10.1111/1471-0528.16380. Epub 2020 Jul 27.
PubMed 32588532Andreasen LA, Tabor A, Norgaard LN, Taksoe-Vester CA, Krebs L, Jorgensen FS, Jepsen IE, Sharif H, Zingenberg H, Rosthoj S, Sorensen AL, Tolsgaard MG. Why we succeed and fail in detecting fetal growth restriction: A population-based study. Acta Obstet Gynecol Scand. 2021 May;100(5):893-899. doi: 10.1111/aogs.14048. Epub 2021 Jan 12.
PubMed 33220065Andreasen LA, Feragen A, Christensen AN, Thybo JK, Svendsen MBS, Zepf K, Lekadir K, Tolsgaard MG. Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization. Sci Rep. 2023 Feb 8;13(1):2221. doi: 10.1038/s41598-023-29105-x.
PubMed 36755050Nicholls D, Sweet L, Hyett J. Psychomotor skills in medical ultrasound imaging: an analysis of the core skill set. J Ultrasound Med. 2014 Aug;33(8):1349-52. doi: 10.7863/ultra.33.8.1349.
PubMed 25063399Govaerts MJ, Schuwirth LW, Van der Vleuten CP, Muijtjens AM. Workplace-based assessment: effects of rater expertise. Adv Health Sci Educ Theory Pract. 2011 May;16(2):151-65. doi: 10.1007/s10459-010-9250-7. Epub 2010 Sep 30.
PubMed 20882335Tolsgaard MG, Pusic MV, Sebok-Syer SS, Gin B, Svendsen MB, Syer MD, Brydges R, Cuddy MM, Boscardin CK. The fundamentals of Artificial Intelligence in medical education research: AMEE Guide No. 156. Med Teach. 2023 Jun;45(6):565-573. doi: 10.1080/0142159X.2023.2180340. Epub 2023 Mar 2.
PubMed 36862064Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7.
PubMed 30617339Tolsgaard MG, Boscardin CK, Park YS, Cuddy MM, Sebok-Syer SS. The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs. Adv Health Sci Educ Theory Pract. 2020 Dec;25(5):1057-1086. doi: 10.1007/s10459-020-10009-8. Epub 2020 Nov 3.
PubMed 33141345Degallier-Rochat S, Kurpicz-Briki M, Endrissat N, Yatsenko O. Human augmentation, not replacement: A research agenda for AI and robotics in the industry. Front Robot AI. 2022 Oct 4;9:997386. doi: 10.3389/frobt.2022.997386. eCollection 2022. No abstract available.
PubMed 36267424Vasey B, Novak A, Ather S, Ibrahim M, McCulloch P. DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology. Clin Radiol. 2023 Feb;78(2):130-136. doi: 10.1016/j.crad.2022.09.131.
PubMed 36639172Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health. 2020 Oct;2(10):e549-e560. doi: 10.1016/S2589-7500(20)30219-3. Epub 2020 Sep 9.
PubMed 33328049Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health. 2020 Oct;2(10):e537-e548. doi: 10.1016/S2589-7500(20)30218-1. Epub 2020 Sep 9.
PubMed 33328048Salomon LJ, Alfirevic Z, Da Silva Costa F, Deter RL, Figueras F, Ghi T, Glanc P, Khalil A, Lee W, Napolitano R, Papageorghiou A, Sotiriadis A, Stirnemann J, Toi A, Yeo G. ISUOG Practice Guidelines: ultrasound assessment of fetal biometry and growth. Ultrasound Obstet Gynecol. 2019 Jun;53(6):715-723. doi: 10.1002/uog.20272.
PubMed 31169958Hadlock FP. Sonographic estimation of fetal age and weight. Radiol Clin North Am. 1990 Jan;28(1):39-50.
PubMed 2404304Borsci S, Federici S, Lauriola M. On the dimensionality of the System Usability Scale: a test of alternative measurement models. Cogn Process. 2009 Aug;10(3):193-7. doi: 10.1007/s10339-009-0268-9. Epub 2009 Jun 30.
PubMed 19565283
Study at a glance
From the registry record, last updated by the sponsor on 10 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Scan-AId: Artificial Intelligence Support in Novice's Decision-making for Assessing Ultrasound Fetal Weight Estimation - A Randomized Trial
- ClinicalTrials.gov ID
- NCT06232187
- Acronym / sponsor ID
- scan-AId / F-24001576
- Conditions
- Fetal Weight, Ultrasound
- Keywords
- Artificial Intelligence, Fetal weight estimation, novices
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 75 participants (estimated)
- Lead sponsor
- Copenhagen Academy for Medical Education and SimulationUniversity, hospital or other
- Collaborators
- Slagelse Hospital, Technical University of Denmark
- Study start
- 14 Feb 2024 (actual)
- Primary completion
- 30 Jun 2024 (estimated)
- Study completion
- 1 Sep 2024 (estimated)
- First posted
- 30 Jan 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 SCAN-AID study is a prospective, randomized, controlled, and unblinded study that compares the performance of novices in ultrasound fetal weight estimation. The study evaluates the impact of two levels of AI support: a straightforward black box AI and a more detailed explainable AI.
Detailed description
The goal of this randomized controlled clinical trial is to learn which type of artificial intelligence (AI) effects the diagnostic accuracy of ultrasound estimation of fetal weight (EFW), when performed by novices, in this study represented by medical students.
The study's objectives are:
- Which type of artificial intelligence support system works for novices in improving the ultrasound fetal weight diagnostic accuracy?
- Does the artificial intelligence improve image quality, evaluate the cognitive load placed on participants when utilizing AI support, and is the AI system usable for novices?
Participants will be tasked with conducting an ultrasound Estimated Fetal Weight (EFW) using either a simple black box AI or a detailed explainable AI feedback system. The AI systems will assist participants in determining if they have captured the appropriate image for EFW. The outcomes will then be compared to those of a control group.
Ultrasound procedures will be performed on pregnant women with fetuses at a gestational age of 28-42 weeks, who have previously undergone an EFW by an expert sonographer or doctor at the clinic within 5 days days leading up to the examinationday. One participant of each randomization arm, will perfrom an EFW on the same pregnant woman.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 3
- Model description
- The participants are allocated to one of three groups: control group, feedback group 1 with black box AI or feedback group 2 with explainable AI feedback.
| Arm | Type | Receives |
|---|---|---|
| Feedback Group 1 (FG1) | Experimental | Behavioral: Artificial Intelligence feedback for ultrasound EFW standard plane imagesParticipatns in FG1 will receive basic black box AI support, with simple explanation like "standard plane", "non standard plane" or "off plane". |
| Feedback Group 2 (FG2) | Experimental | Behavioral: Artificial Intelligence feedback for ultrasound EFW standard plane imagesParticipants in FG2 will receive explainable AI support, with more elaborate description of the anatomical structures and segmentation of the anatomy. |
| Control group (CG) | No intervention | –Participants in the CG will have a standard plane poster to help guide them to the EFW ultrasound standard plane images. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- Medical students with no former fetal or abdominal ultrasound training.
- The participants will have to understand spoken and written Danish or English.
- The participants will have to understand spoken and written Danish or English.
- BMI < 30
- Gestational age: 28-42
Key exclusion criteria (6 listed)
- Medical students who received formal fetal or abdominal training prior to the inclusion in this study.
- Pregnant women;
- Age > 40 years
- Fefal anomaly
- Oligohydramnion
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 |
|---|---|---|
| Artificial Intelligence feedback for ultrasound EFW standard plane imagesAI feedback in two levels, in aid of the participants, to obtain the right standardplane images used in fetal ultrasound EFW calculation. | Behavioural | Feedback Group 1 (FG1), Feedback Group 2 (FG2) |
Primary outcome
Diagnostic accuracy
Time frame: 15 minutes
The accuracy in each group was defined as the percentage difference between estimated fetal weight and the sonographer expert EFW
1 secondary outcome
Image Quality
Time frame: 5 minutes pr. participant
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 May 2024.
- Background: PubMed 32588532Andreasen LA, Tabor A, Norgaard LN, Rode L, Gerds TA, Tolsgaard MG. Detection of growth-restricted fetuses during pregnancy is associated with fewer intrauterine deaths but increased adverse childhood outcomes: an observational study. BJOG. 2021 Jan;128(1):77-85. doi: 10.1111/1471-0528.16380. Epub 2020 Jul 27.
- Background: PubMed 33220065Andreasen LA, Tabor A, Norgaard LN, Taksoe-Vester CA, Krebs L, Jorgensen FS, Jepsen IE, Sharif H, Zingenberg H, Rosthoj S, Sorensen AL, Tolsgaard MG. Why we succeed and fail in detecting fetal growth restriction: A population-based study. Acta Obstet Gynecol Scand. 2021 May;100(5):893-899. doi: 10.1111/aogs.14048. Epub 2021 Jan 12.
- Background: PubMed 36755050Andreasen LA, Feragen A, Christensen AN, Thybo JK, Svendsen MBS, Zepf K, Lekadir K, Tolsgaard MG. Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization. Sci Rep. 2023 Feb 8;13(1):2221. doi: 10.1038/s41598-023-29105-x.
- Background: PubMed 25063399Nicholls D, Sweet L, Hyett J. Psychomotor skills in medical ultrasound imaging: an analysis of the core skill set. J Ultrasound Med. 2014 Aug;33(8):1349-52. doi: 10.7863/ultra.33.8.1349.
- Background: PubMed 20882335Govaerts MJ, Schuwirth LW, Van der Vleuten CP, Muijtjens AM. Workplace-based assessment: effects of rater expertise. Adv Health Sci Educ Theory Pract. 2011 May;16(2):151-65. doi: 10.1007/s10459-010-9250-7. Epub 2010 Sep 30.
- Background: PubMed 36862064Tolsgaard MG, Pusic MV, Sebok-Syer SS, Gin B, Svendsen MB, Syer MD, Brydges R, Cuddy MM, Boscardin CK. The fundamentals of Artificial Intelligence in medical education research: AMEE Guide No. 156. Med Teach. 2023 Jun;45(6):565-573. doi: 10.1080/0142159X.2023.2180340. Epub 2023 Mar 2.
- Background: PubMed 30617339Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7.
- Background: PubMed 33141345Tolsgaard MG, Boscardin CK, Park YS, Cuddy MM, Sebok-Syer SS. The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs. Adv Health Sci Educ Theory Pract. 2020 Dec;25(5):1057-1086. doi: 10.1007/s10459-020-10009-8. Epub 2020 Nov 3.
- Background: PubMed 36267424Degallier-Rochat S, Kurpicz-Briki M, Endrissat N, Yatsenko O. Human augmentation, not replacement: A research agenda for AI and robotics in the industry. Front Robot AI. 2022 Oct 4;9:997386. doi: 10.3389/frobt.2022.997386. eCollection 2022. No abstract available.
- Background: PubMed 36639172Vasey B, Novak A, Ather S, Ibrahim M, McCulloch P. DECIDE-AI: a new reporting guideline and its relevance to artificial intelligence studies in radiology. Clin Radiol. 2023 Feb;78(2):130-136. doi: 10.1016/j.crad.2022.09.131.
- Background: PubMed 33328049Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health. 2020 Oct;2(10):e549-e560. doi: 10.1016/S2589-7500(20)30219-3. Epub 2020 Sep 9.
- Background: PubMed 33328048Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health. 2020 Oct;2(10):e537-e548. doi: 10.1016/S2589-7500(20)30218-1. Epub 2020 Sep 9.
- Individual participant dataSharing planned: No
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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 Artificial Intelligence feedback for ultrasound EFW standard plane images in people with Fetal Weight and Ultrasound, run by Copenhagen Academy for Medical Education and Simulation at 1 site in Denmark.
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 5 inclusion and 6 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: status not confirmed, as of the sponsor's last update on 10 May 2024. 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 Denmark. 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?
Results are not available in the source record.
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?
Diagnostic accuracy (time frame: 15 minutes). 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 NCT06232187, which also lists 16 linked publications.
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 10 May 2024.