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

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

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.

Study Overview

• Status: Recruiting
• Conditions: Preterm Birth; Artificial Intelligence (AI) in Diagnosis
• Intervention / Treatment: Other: Cervical ultrasound image acquisition

• Study Type: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

• Study Contact: Name: Emilie Pi F Sejer, MD; Phone Number: 0045 28890690; Email: emilie.pi.fogtmann.sejer.01@regionh.dk

Study Locations

• Denmark
  • Copenhagen, Denmark, 2100
    • Recruiting
    • Rigshospitalet, Copenhagen Univeristy Hospital
    • Contact: Emilie Pi F Sejer, MD; Phone Number: 0045 28890690; Email: emilie.pi.fogtmann.sejer.01@regionh.dk

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

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

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; Acquisition of cervical ultrasound images with variation in image acquisition parameters.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Time Frame
Spontaneous preterm birth <34+0 and <32+0 weeks	At delivery.

Other Outcome Measures

Outcome Measure	Time Frame
Any preterm birth (including medically indicated)	At delivery.
Time-to-delivery from scan	From second-trimester scan until delivery.

Collaborators and Investigators

• Sponsor: Rigshospitalet, Denmark
• Collaborators: Technical University of Denmark; Department of Computer Science, University of Copenhagen, Denmark; Sundhedsdonationer
• Investigators: Study Chair: Martin G Tolsgaard, MD, PhD, DMSc, Department of Obstetrics and Gynecology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark

Study record dates

Study Major Dates

• Study Start (Actual): March 11, 2026
• Primary Completion (Estimated): January 1, 2027
• Study Completion (Estimated): February 1, 2027

Study Registration Dates

• First Submitted: May 12, 2026
• First Submitted That Met QC Criteria: May 19, 2026
• First Posted (Actual): May 20, 2026

Study Record Updates

• Last Update Posted (Actual): May 20, 2026
• Last Update Submitted That Met QC Criteria: May 19, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: Artificial intelligence; Diagnostic accuracy; Premature birth; Preterm birth; Deep learning; Prospective validation; Image acquisition; Cervical ultrasound
• Additional Relevant MeSH Terms: Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Obstetric Labor, Premature; Obstetric Labor Complications; Pregnancy Complications; Premature Birth
• Other Study ID Numbers: P-2026-21388

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No