aceAI-Powered CURA Application for Identifying At-Risk Pregnancies in Obstetrics Management (CURAte): protocol for a randomised controlled trial
Background: Early identification of high-risk pregnancies is a cornerstone of obstetric care, yet antenatal risk assessment in routine practice remains imprecise and highly variable. Evidence from contemporary cohort studies suggests that only a minority of pregnancies that subsequently experience adverse maternal or fetal/neonatal outcomes are identified as high risk in the first trimester, with reported detection rates in the range of 20%–30%. Despite rapid advances in machine-learning (ML) methods for clinica…
Routine antenatal risk assessment is imprecise: only about 20%–30% of pregnancies with later adverse outcomes are flagged as high risk in the first trimester, and no ML approach has been tested against standard care in an RCT.
This is a protocol only, so no outcome data exist; the hypothesised gains in accuracy and reduced adverse outcomes are untested, and the tool is intended only as decision support.
Evidence
- Peer-reviewedBMJ Open2026-10-20
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Truvace Impact Record TRV-2026-2154, v1: “AI-Powered CURA Application for Identifying At-Risk Pregnancies in Obstetrics Management (CURAte): protocol for a randomised controlled trial.” Truvace, 2026-10-22. /record/TRV-2026-2154 (accessed at citation time). sha256 89152b0eda93c2c7…
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