RecruitingNCT07598097

The Impact of Image Acquisition in Cervical Ultrasound on AI-Based Prediction of Preterm Birth in Clinical Practice: A Prospective Observational Study

Sponsor: Rigshospitalet, Denmark

Trial Details

Condition	Preterm Birth, Artificial Intelligence (AI) in Diagnosis
Intervention	Cervical ultrasound image acquisition
Status	Recruiting
Participants	2,000
Est. Completion	1 Jan 2027
Last Updated	20 May 2026

Primary Outcome

Spontaneous preterm birth <37+0 weeks

Eligibility Criteria

Inclusion Criteria

Inclusion Criteria: * Pregnant women aged ≥18 years * Attending routine second-trimester scan (and scheduled transvaginal cervical assessment per local protocol/workflow) Exclusion Criteria: * Absence of transvaginal cervical assessment at the second-trimester scan * Missing follow-up data on pregnancy outcome (gestational age at delivery) * Inadequate image quality or missing required cervical ultrasound image

Trial Sites

Countries

Denmark

View Registry Entry

Related Analysis

Danish Study Evaluates AI Preterm Birth Prediction Across Different Cervical Ultrasound Settings — Clinical Practice Implications

Danish researchers are evaluating how different ultrasound imaging settings affect AI model performance in predicting preterm birth. This 2,000-patient study addresses critical questions about implementing AI diagnostic tools in routine obstetric practice.

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