Evaluating the Sensitivity to Change of AI-Feedback in Ultrasound Biometry: A Stratified Randomized Controlled Trial Across the Expertise Gradient
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 75
- 试验地点
- 3
- 主要终点
- To evaluate the sensitivity to change in ultrasound measurement accuracy when using AI-feedback compared to standard scanning
研究概览
简要总结
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).
详细描述
Study Overview: This study evaluates how real-time Artificial Intelligence (AI) feedback impacts the accuracy of fetal biometry measurements in obstetric ultrasound. While AI tools are designed to assist clinicians, their effectiveness may vary depending on the user's baseline skill level-a phenomenon known as the "Expertise Reversal Effect."
Research Aim: The primary objective is to determine if AI-guided feedback significantly reduces measurement error in ultrasound fetal weight estimation to traditional manual methods. The study specifically investigates whether the benefit of AI is greater for novice users, intermediate users users than for experienced specialists.
Study Design: This is a stratified, randomized controlled trial involving 75 participants categorized into three expertise tiers:
Novices (e.g., students or residents with minimal scan experience).
Intermediate Users (e.g., physicians in mid-level training).
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Single (Outcomes Assessor)
盲法说明
Image quality assessors
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Clinical Target Population: Healthcare professionals and students, including but not limited to:
- •Medical students (doing their masters.
- •Resident physicians and Senior Consultants in Obstetrics and Gynecology.
- •- If the participants do not understand and speak either Danish or English
- •Pregnant women:
- •Inclusion Criteria:
- •Pre pregnancy BMI < 40
- •Singelton pregnancy
- •GA ≥ 37+0 at time of induction
- •Intact membranes (to ensure consistent amniotic fluid index)
排除标准
- •Major fetal anatomical anomaly
- •Anhydramnios (DVP < 2 cm)
- •CPR ratio < 2.5th percentile
研究组 & 干预措施
AI intervention Group
The software provides real-time "traffic light" or score-based feedback to validate when the correct anatomical plane (BPD, HC, AC, or FL) has been reached.
干预措施: AI interventional group (Device)
Control Group
Participants in the control arm perform fetal biometry using standard manual techniques without any AI assistance.
结局指标
主要结局
To evaluate the sensitivity to change in ultrasound measurement accuracy when using AI-feedback compared to standard scanning
时间窗: The two scans will be performed within a timeframe of 14 days.
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.
次要结局
- Experience Threshold for AI-Mediated Accuracy Gains(Through study completion, an average of 1 year.)
- Procedural Efficacy(The duration of the scan, maximum of 30 minutes)
- Image Quality(Through study completion, an average of 1 year.)
- Cognitive and Physiological Load(During the ultrasound procedure (GSR) and immediately following the procedure (NASA-TLX), approximately 30 minutes in total.)
- Measurement Deviation:(The duration from pre study scan and study scan.)
研究者
Mary Le Ngo
MD, PhD student
Copenhagen Academy for Medical Education and Simulation