RecruitingNot ApplicableNCT06371859

Human-AI Collaborative Intelligence for Improving Fetal Flow Management

Human-AI Collaborative Intelligence for Improving Fetal Flow Management: A Randomized Trial

Sponsor

Rigshospitalet, Denmark

Enrollment

92 participants

Start Date

Apr 29, 2024

Study Type

INTERVENTIONAL

Conditions

Healthy

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.

Eligibility

Inclusion Criteria1

The inclusion criterion is the use of ultrasound at least once per week

Exclusion Criteria1

The exclusion criterion is the absence of experience in ultrasound scanning.

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Interventions

BEHAVIORAL"XAI feedback on MCA/UA Doppler spectral curves and gate placement suggestions"

This study includes 1840 ultrasound images, split into UA and MCA flow and spectrum images, each duplicated for a total of 3680 images to compare explainable AI (XAI) feedback vs. no feedback. The investigators will provide matched sets of 40 images (one for the XAI group and one for the non-XAI group) to participants. Participants are matched based on their level of experience within each hospital (Resident physicians, obstetricians, and gynecologists with obstetric ultrasound experience). All participants are instructed to place gates on the flow images of the umbilical artery and the middle cerebral artery and to assess the quality of the resulting flow curves. Specifically, for flow images, participants must identify the most appropriate gate placement. For spectral flow curves, they are to decide if the curves are of sufficient quality to guide medical management decisions.