Human-AI Collaborative Intelligence for Improving Fetal Flow Management: A Randomized Trial
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 92
- 试验地点
- 4
- 主要终点
- Responses will be reviewed independently by two fetal medicine sonographers, and in case of disagreement between the two experts, a consensus will be reached.
研究概览
简要总结
This randomized controlled study evaluates the effectiveness of explainable AI (XAI) in improving clinicians' interpretation of Doppler ultrasound images (UA and MCA) in obstetrics. It involves 92 clinicians, randomized into intervention and control groups. The intervention group receives XAI feedback, aiming to enhance accuracy in ultrasound interpretation and medical decision-making.
Objectives:
- To develop an interpretable model for commonly used Doppler flows, specifically the Pulsatility Index (PI) of the umbilical artery (UA) and middle cerebral artery (MCA), with the aim to provide quality feedback on Doppler spectrum images and suggest potential gate placements.
- To test the effects of providing Explainable AI (XAI)-feedback for clinicians compared with no feedback on their accuracy in ultrasound interpretation and management.
详细描述
Currently, Doppler ultrasound velocimetry serves as a crucial tool in obstetric practice, particularly for assessing the umbilical artery (UA) and middle cerebral artery (MCA) in uteroplacental-fetal circulation. While Doppler ultrasound is valuable for detecting conditions like fetal anemia and placental insufficiency, its accuracy relies on operator expertise. Artificial intelligence (AI) offers potential enhancements, especially in high-risk pregnancies. However, existing AI applications in fetal ultrasound often lack transparency, leading to user distrust. This study aims to address these limitations by developing an explainable AI model to assist clinicians in interpreting Doppler ultrasound images of UA and MCA for improved management of high-risk pregnancies.
The study's objectives are:
- To develop an interpretable model for commonly used Doppler flows, specifically the Pulsatility Index (PI) of the umbilical artery (UA) and middle cerebral artery (MCA), with the aim to provide quality feedback on Doppler spectrum images and suggest potential gate placements.
- To test the effects of providing Explainable AI (XAI)-feedback for clinicians compared with no feedback on their accuracy in ultrasound interpretation and management.
All participants will be instructed to provide gate placement for flow images of the umbilical artery and the MCA, and to evaluate the quality of the resulting flow curves. Each participant will be required to evaluate a total of 40 unique images (10 flow images for UA and MCA, 10 spectral doppler images for UA and MCA, respectively). From the four groups (UA-flow, UA-spectrum, MCA-flow & MCA-spectrum) the investigators will provide matched sets of 40 images that are provided to participants, who are matched for their level of experience within each hospital (PGY 1-2; PGY 3-5; board certified Obstetricians). For flow images, the participants will be instructed to identify the most appropriate gate placement. For the spectral flow curves, participants will be asked to evaluate whether the flow curves were of sufficient quality to inform medical management decisions.
The inclusions criteria for MCA and UA images will be images taken from the third trimester (>= week 28).
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Health Services Research
- 盲法
- Double (Participant, Investigator)
盲法说明
Sealed envelopes will be used and opened at the time of inclusion of each participant. Each participant will be randomized to either AI support or no support. The allocation of participants will be performed by (XYZ) who does not have access to the randomization order.
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •The inclusion criterion is the use of ultrasound at least once per week
排除标准
- •The exclusion criterion is the absence of experience in ultrasound scanning.
结局指标
主要结局
Responses will be reviewed independently by two fetal medicine sonographers, and in case of disagreement between the two experts, a consensus will be reached.
时间窗: 1 months
The accuracy in each group (AI-feedback and without AI-feedback group) was defined as the percentage difference in the number of correctly managed flow images between the two groups, assessed by two fetal medicine sonographers. Correct management was defined as: Correct gate placement (multiple sites possible) AND Correct identification of flow curves that were of adequate quality to allow medical decision-making.
次要结局
- Accuracy of flow image management among competence groups(1 months)
研究者
Zahra Bashir
Ph.d.student, medical doctor and primary investigator
Rigshospitalet, Denmark