The Impact of Image Acquisition in Cervical Ultrasound on AI-Based Prediction of Preterm Birth in Clinical Practice

NCT07598097 | Recruiting

• 1 other identifier: P-2026-21388
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief Summary

This study prospectively evaluates whether the performance of an already-developed artificial intelligence (AI) model for predicting spontaneous preterm birth changes when cervical ultrasound images are obtained using different ultrasound image settings. The primary research question is whether the AI model performs differently across images acquired with different imaging settings.

Conditions

Preterm Birth; Artificial Intelligence (AI) in Diagnosis

Keywords

Preterm birth; Premature birth; Artificial intelligence; Deep learning; Image acquisition; Diagnostic accuracy; Cervical ultrasound; Prospective validation

Outcome Measures

Primary Outcomes (1)

• Spontaneous preterm birth <37+0 weeks

  Birth \<37+0 weeks after spontaneous onset of labor (with or without preterm prelabor rupture of membranes \[PPROM\]), regardless of mode of delivery, and excluding medically indicated (iatrogenic) preterm births without spontaneous onset.

  At delivery.

Secondary Outcomes (1)

• Spontaneous preterm birth <34+0 and <32+0 weeks

  At delivery.

Other Outcomes (2)

• Any preterm birth (including medically indicated)

  At delivery.

• Time-to-delivery from scan

  From second-trimester scan until delivery.

Study Arms (1)

Pregnant women attending routine second-trimester scan

Pregnant women aged ≥18 years attending routine second-trimester scan (and scheduled transvaginal cervical assessment per local protocol/workflow).

Other: Cervical ultrasound image acquisition

Interventions

Cervical ultrasound image acquisition OTHER

Acquisition of cervical ultrasound images with variation in image acquisition parameters.

Pregnant women attending routine second-trimester scan

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Pregnant women attending routine second-trimester scan at Rigshospitalet from March to September, 2026.

You may qualify if:

• Pregnant women aged ≥18 years
• Attending routine second-trimester scan (and scheduled transvaginal cervical assessment per local protocol/workflow)

You may not qualify if:

• 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

Sponsors & Collaborators

• Sundhedsdonationer: collaborator
• Rigshospitalet, Denmark: lead
• Department of Computer Science, University of Copenhagen, Denmark: collaborator
• Technical University of Denmark: collaborator

Study Sites (1)

Rigshospitalet, Copenhagen Univeristy Hospital

Copenhagen, 2100, Denmark

RECRUITING

MeSH Terms

Conditions

Premature Birth

Condition Hierarchy (Ancestors)

Obstetric Labor, Premature; Obstetric Labor Complications; Pregnancy Complications; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases

Study Officials

• Martin G Tolsgaard, MD, PhD, DMSc

  Department of Obstetrics and Gynecology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark

  STUDY CHAIR

Central Study Contacts

Emilie Pi F Sejer, MD

CONTACT

0045 28890690; emilie.pi.fogtmann.sejer.01@regionh.dk

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: May 12, 2026
• First Posted: May 20, 2026
• Study Start: March 11, 2026
• Primary Completion (Estimated): January 1, 2027
• Study Completion (Estimated): February 1, 2027
• Last Updated: May 20, 2026

Record last verified: 2026-05

Locations

DK (1)