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FAST-AI eHTA

Towards Fully automated Anomaly Screening in the first Trimester of pregnancy using Artificial Intelligence: early-stage Health Technology Assessment - FAST-AI eHTA

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
NL-OMON
Registry ID
NL-OMON58594
Enrollment
180
Registered
2026-04-09
Start date
2026-05-07
Completion date
Unknown
Last updated
2026-05-18

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Not applicable Not applicable

Interventions

Not applicable.

Sponsors

Erasmus MC, Universitair Medisch Centrum Rotterdam
Lead Sponsor

Eligibility

Age
18 Years to 99 Years

Inclusion criteria

Inclusion criteria: For Expert Interviews and Focus Groups:Relevant (Healthcare) Professionals: Participants must be obstetricians, gynecologists, sonographers, ultrasound technicians, midwives, ethicists, economists, patient representatives or other stakeholders involved in or influenced by first-trimester screening or the integration of AI in clinical practice.For questionnaire:Stakeholder Influence: Individuals whose roles could influence or could be influenced by the use of AI in radiology and first-trimester screening.

Exclusion criteria

Exclusion criteria: A potential subject who meets any of the following criteria will be excluded from participation in this study: For Expert Interviews and Focus Groups:Individuals who are unwilling to participate in recording. inability to understand English or DutchFor Questionnaire:Individuals who are unwilling to complete the questionnaire.inability to understand English or Dutch

Design outcomes

Primary

MeasureTime frame
The primary endpoint of this study is the development of a comprehensive early Health Technology Assessment (eHTA) report for AI-supported first-trimester anomaly screening.

Secondary

MeasureTime frame
To identify and prioritize criteria that determine the value of AI in first-trimester screening;To develop and compare alternative AI design strategies using MCDA;To assess stakeholder perspectives on facilitators, barriers, and requirements for implementation;To estimate the relative importance (weights) of evaluation criteria using AHP.

Countries

Netherlands

Contacts

Public ContactM. Rousian

Erasmus MC, Universitair Medisch Centrum Rotterdam

a.m.f.b.akerboom@erasmusmc.nl010-7040704

Outcome results

None listed

Source: NL-OMON (via WHO ICTRP) · Data processed: May 22, 2026