Health TrialsbyHealthPanel

Impact of AI Feedback on Ultrasound Biometry Accuracy Across the Expertise Levels

ConditionsFetal Growth AbnormalitiesFetal Weight
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
AgeNot listed
SponsorCopenhagen Academy for Medical Education and Simulation

About this trial

Objective: To evaluate the impact of real-time AI feedback on fetal biometry accuracy and investigate the Expertise Reversal Effect-whether AI benefits diminish as user experience increases.

Design: A stratified randomized trial of 75 participants (25 Novices, 25 Intermediates, 25 Experts). Users are randomized 1:1 to either AI-assisted or manual measurement groups.

Outcomes:

* Primary: EFW accuracy (MAPE) compared to actual birthweight. * Secondary: Procedure time, image quality, error relative to baseline scans, and cognitive workload (NASA-TLX).

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

Medical students (doing their masters.

Resident physicians and Senior Consultants in Obstetrics and Gynecology.

Pre pregnancy BMI < 40

Singelton pregnancy

Disqualifiers

Major fetal anatomical anomaly

Anhydramnios (DVP < 2 cm)

CPR ratio < 2.5th percentile

Trial design

Design model

Parallel

Treatments tested in this trial

  • AI interventional group

    Device

    Participants in the intervention arm perform fetal biometry with the assistance of real-time Artificial Intelligence (AI) feedback software.

Treatment groups

75 Participants
are divided into 2 treatment groups
Group A: Control GroupNo intervention 0 interventions
Group B: AI intervention GroupExperimental treatment 1 intervention

Trial outcomes

Primary outcomes

1

To evaluate the sensitivity to change in ultrasound measurement accuracy when using AI-feedback compared to standard scanning

Mean absolute percentage error (MAPE), defined as the absolute difference between estimated fetal weight (EFW) and actual birth weight (ABW) divided by actual birth weight and expressed as a percentage, for AI-assisted and manual fetal biometry.

Time frame
The two scans will be performed within a timeframe of 14 days.

Secondary outcomes

1

Procedural Efficacy

Scan duration (seconds) will be modeled as the dependent variable to assess the tem-poral impact of the AI-feedback.

Time frame
The duration of the scan, maximum of 30 minutes
2

Image Quality

Salomon Quality Score on the 16-point fetal biometry plane quality scale, comparing AI-assisted and manual ultrasound acquisition. The scale ranges from 0 to 16, with higher scores indicating better anatomical plane quality.

Time frame
Through study completion, an average of 1 year.
3

Cognitive and Physiological Load

Both the NASA-TLX (subjective) and GSR (objective) data will be modeled as de-pendent variables. These analyses will determine if the AI-intervention significantly alters the mental effort or autonomic stress response during the procedure.

Time frame
During the ultrasound procedure (GSR) and immediately following the procedure (NASA-TLX), approximately 30 minutes in total.
4

Measurement Deviation:

The absolute difference between the participant's EFW and a baseline EFW performed of an experienced clinician, will be modeled to assess if AI reduces inter-observer variability.

Time frame
The duration from pre study scan and study scan.

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Copenhagen Academy for Medical Education and Simulation

Lead sponsor

Rigshospitalet, Denmark

Collaborator

Slagelse Hospital

Collaborator

Source ClinicalTrials.gov
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