AI Support in Novice's Decision-making for Ultrasound Fetal Weight Estimation
NCT ID: NCT06232187
Last Updated: 2024-05-10
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ENROLLING_BY_INVITATION
NA
75 participants
INTERVENTIONAL
2024-02-14
2024-09-01
Brief Summary
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Detailed Description
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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
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Study Design
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RANDOMIZED
PARALLEL
control group, feedback group 1 with black box AI or feedback group 2 with explainable AI feedback.
DIAGNOSTIC
SINGLE
Study Groups
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Feedback Group 1 (FG1)
Participatns in FG1 will receive basic black box AI support, with simple explanation like "standard plane", "non standard plane" or "off plane".
Artificial Intelligence feedback for ultrasound EFW standard plane images
AI feedback in two levels, in aid of the participants, to obtain the right standardplane images used in fetal ultrasound EFW calculation.
Feedback Group 2 (FG2)
Participants in FG2 will receive explainable AI support, with more elaborate description of the anatomical structures and segmentation of the anatomy.
Artificial Intelligence feedback for ultrasound EFW standard plane images
AI feedback in two levels, in aid of the participants, to obtain the right standardplane images used in fetal ultrasound EFW calculation.
Control group (CG)
Participants in the CG will have a standard plane poster to help guide them to the EFW ultrasound standard plane images.
No interventions assigned to this group
Interventions
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Artificial Intelligence feedback for ultrasound EFW standard plane images
AI feedback in two levels, in aid of the participants, to obtain the right standardplane images used in fetal ultrasound EFW calculation.
Eligibility Criteria
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Inclusion Criteria
* 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
Exclusion Criteria
Pregnant women;
* Age \> 40 years
* Fefal anomaly
* Oligohydramnion
* Gestational Diabetes, Diabetes type 1 or 2.
18 Years
ALL
Yes
Sponsors
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Slagelse Hospital
OTHER
Technical University of Denmark
OTHER
Copenhagen Academy for Medical Education and Simulation
OTHER
Responsible Party
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Mary Le Ngo
Doctor of medicine, PhD student
Locations
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Rigshospitalet
Copenhagen, , Denmark
Countries
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References
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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.
Andreasen 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.
Andreasen 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.
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Cruz 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.
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Salomon 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.
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Other Identifiers
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F-24001576
Identifier Type: -
Identifier Source: org_study_id
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