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

Ultrasound Clinical Trial

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

Clinical 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.

Clinical Trial 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 30-37 weeks, who have previously undergone an EFW by an expert sonographer at the clinic either on the same day or the day before. One participant of each randomization arm, will perfrom an EFW on the same pregnant woman. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT06232187
Study type Interventional
Source Copenhagen Academy for Medical Education and Simulation
Contact
Status Enrolling by invitation
Phase N/A
Start date February 14, 2024
Completion date September 1, 2024
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