Two methods for capturing second‑trimester ultrasound images.

A Single-center Cross-sectional Study Comparing Two Image Acquisition Modalities for Second-trimester Pregnancy Screening Ultrasound.

Not applicable Interventional Clinique Rive Gauche · NCT07286591

This test will see if using an AI-guided image acquisition method improves the completeness and quality of routine second‑trimester (20+0 to 24+6 weeks) anatomy ultrasounds for pregnant people with a single, healthy pregnancy.

Quick facts

PhaseNot applicable
Study typeInterventional
Enrollment50 (estimated)
Ages18 Years and up
SexFemale
SponsorClinique Rive Gauche Academic / other
Locations1 site (Toulouse)
Trial IDNCT07286591 on ClinicalTrials.gov

What this trial studies

The trial compares standard operator-driven ultrasound image acquisition with an ultrasound protocol assisted by artificial intelligence during the routine second‑trimester morphology scan. Eligible participants are pregnant people aged 18 or older with a single, viable pregnancy dated by first‑trimester ultrasound and no known fetal malformations, scanned between 20+0 and 24+6 weeks. Images from each modality will be compared against recommended reference views to measure completeness, image quality, and adherence to the 26‑view French CNEOF recommendations. Study procedures take place at a single center and focus on whether AI guidance can standardize acquisition and reduce operator dependence.

Who should consider this trial

Good fit: Pregnant people aged 18 or older with a single, viable pregnancy dated by first‑trimester ultrasound, no known malformations, scheduled for routine second‑trimester screening at 20+0 to 24+6 weeks, able to consent, and covered by French social security are ideal candidates.

Not a fit: People with multiple pregnancies, known fetal malformations, pathological pregnancies, cognitive impairment or difficulty consenting, those under legal guardianship, or those without health insurance are excluded and unlikely to benefit from participation.

Why it matters

Potential benefit: If successful, the AI-guided method could make second‑trimester scans more consistent and complete, helping clinicians detect structural issues earlier and more reliably.

How similar studies have performed: Standardization efforts and several studies show that strict imaging protocols improve detection rates, and early pilot work with AI-assisted ultrasound has shown promise, though widespread use in routine second‑trimester screening is still relatively new.

Eligibility criteria

Show full inclusion / exclusion criteria
Inclusion Criteria:

* Women aged 18 or over,
* With a single, viable pregnancy, definitively dated by first-trimester ultrasound, with no known malformations,
* Scheduled for a routine second-trimester screening ultrasound, i.e., between 20 weeks + 0 days and 24 weeks + 6 days,
* Having given their informed consent,
* Affiliated with the social security system or a beneficiary of such a plan.

Exclusion Criteria:

* Multiple pregnancy,
* Known fetal malformation,
* Pathological pregnancy,
* Cognitive impairment, or a disorder causing difficulty understanding instructions or answering questionnaires,
* Patient under legal guardianship,
* Patient not covered by health insurance,
* Protected patient: adult under guardianship, curatorship, or other legal protection, deprived of liberty by judicial or administrative decision

Where this trial is running

Toulouse

Study contacts

How to participate

  1. Review the eligibility criteria above with your treating physician.
  2. Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
  3. Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions Artifical IntelligenceEchography Ultrasound
Last reviewed 2026-06-09 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.