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AI Support in Novice's Decision-making for Ultrasound Fetal Weight Estimation

Scan-AId: Artificial Intelligence Support in Novice's Decision-making for Assessing Ultrasound Fetal Weight Estimation - A Randomized Trial

Status
Enrolling by invitation
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06232187
Acronym
scan-AId
Enrollment
75
Registered
2024-01-30
Start date
2024-02-14
Completion date
2024-09-01
Last updated
2024-05-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Fetal Weight, Ultrasound

Keywords

Artificial Intelligence, Fetal weight estimation, novices

Brief 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.

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.

Interventions

BEHAVIORALArtificial 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.

Sponsors

Slagelse Hospital
CollaboratorOTHER
Technical University of Denmark
CollaboratorOTHER
Copenhagen Academy for Medical Education and Simulation
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Outcomes Assessor)

Masking description

The ultrasound images will receive quality scoring from an experienced fetal medicin consultant. Theese are blinded for which intervention the participant received.

Intervention 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.

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

Ultrasound novice participants: Inclusion Criteria: * Medical students with no former fetal or abdominal ultrasound training. * The participants will have to understand spoken and written Danish or English.

Exclusion criteria

• Medical students who received formal fetal or abdominal training prior to the inclusion in this study. Pregnant women; Inclusion Criteria: * The participants will have to understand spoken and written Danish or English. * BMI \< 30 * Gestational age: 28-42

Design outcomes

Primary

MeasureTime frameDescription
Diagnostic accuracy15 minutesThe accuracy in each group was defined as the percentage difference between estimated fetal weight and the sonographer expert EFW

Secondary

MeasureTime frameDescription
Image Quality5 minutes pr. participantSalomon 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.

Other

MeasureTime frameDescription
The AI system usability5 minutesThe participants will be asked to answer a questionnaire: System Usability Scale (SUS), which is used to evaluate the AI feedback system. Min 1 Maximum 100. A higher score indicating better system usability.
Measurement of the reaction time5 minutesMeasurements of the participants reaction time will a measurement for the cognitive load. The reaction time will be measured as a secondary task while the participants are performing the ultrasound scan.

Countries

Denmark

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026