Ignite Creation Date:
2024-05-06 @ 8:25 PM
Last Modification Date:
2024-10-26 @ 3:27 PM
Study NCT ID:
NCT06371859
Status:
RECRUITING
Last Update Posted:
2024-05-06
First Post:
2023-05-09
Brief Title:
Human-AI Collaborative Intelligence for Improving Fetal Flow Management
Sponsor:
Rigshospitalet Denmark
Organization:
Rigshospitalet Denmark
Study Overview
Official Title:
Human-AI Collaborative Intelligence for Improving Fetal Flow Management A Randomized Trial
Status:
RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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
1 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
2 To test the effects of providing Explainable AI XAI-feedback for clinicians compared with no feedback on their accuracy in ultrasound interpretation and management
Objectives
1 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
2 To test the effects of providing Explainable AI XAI-feedback for clinicians compared with no feedback on their accuracy in ultrasound interpretation and management
Detailed Description:
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 studys objectives are
1 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
2 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
Study Design Randomized controlled trial
Data Source 1840 unique ultrasound scans including umbilical artery UA and middle cerebral artery MCA measurements The 1840 unique images includes 460 images of UA-flow images 460 UA-spectrum images 460 MCA-flow images and 460 MCA-spectrum images
Participants 92 clinicians with varying competence levels across four different university hospitals
Intervention XAI feedback on MCAUA Doppler spectral curves and gate placement suggestions
Control Group No XAI feedback
Procedure Clinicians will be divided into two groups of 46 each matched for experience levels across hospitals The control group will place a gate on MCAUA images and evaluate the Doppler spectrum without AI feedback while the intervention group will perform the same tasks with access to AI feedback
Outcome Measures The participants responses in the two groups are reviewed by two fetal medicine sonographers who evaluate the participants answers independently of each other In a disagreement the two sonographers reach a consensus after discussion
The studys objectives are
1 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
2 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
Study Design Randomized controlled trial
Data Source 1840 unique ultrasound scans including umbilical artery UA and middle cerebral artery MCA measurements The 1840 unique images includes 460 images of UA-flow images 460 UA-spectrum images 460 MCA-flow images and 460 MCA-spectrum images
Participants 92 clinicians with varying competence levels across four different university hospitals
Intervention XAI feedback on MCAUA Doppler spectral curves and gate placement suggestions
Control Group No XAI feedback
Procedure Clinicians will be divided into two groups of 46 each matched for experience levels across hospitals The control group will place a gate on MCAUA images and evaluate the Doppler spectrum without AI feedback while the intervention group will perform the same tasks with access to AI feedback
Outcome Measures The participants responses in the two groups are reviewed by two fetal medicine sonographers who evaluate the participants answers independently of each other In a disagreement the two sonographers reach a consensus after discussion
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None