AI blind-sweep obstetric ultrasound by non-specialists in rural DRC

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational study evaluates the diagnostic accuracy and feasibility of a AI-assisted blind-sweep obstetric ultrasound (SPAQ E-con AI) operated by trained non-specialist health workers during antenatal screening in rural DRC. It follows a Hybrid Type 1 design to assess both effectiveness and implementation, with model updates planned after interim analyses. Primary outcomes focus on gestational age estimation accuracy and AI confidence calibration, using manual ultrasound measurements as the reference standard. Enrollment targets include about 1,380 participants across two batches, with a ceiling of 3,000 and potential early stop if predefined criteria are met. The study is conducted at up to six facilities in the Kenge health zone, Kwango province, plus village outreach sites, and includes sub-cohorts for early and late gestational ages.

Why it matters

Who can join: the full rules

Inclusion Criteria:

* Pregnant women aged 18 years or older
* Identified at a participating facility routine ANC visit or RECO village outreach within the Kenge health zone catchment
* Written informed consent obtained

Exclusion Criteria:

* Emergency presentation
* Duplicate re-registration of an already-enrolled woman
* Lacking capacity to consent
* Planned relocation outside Kwango province during the study period
* Refusal of consent

Where it's running

• Centre de Sante CBCO
  Kenge, Kwango, Democratic Republic of the Congo Recruiting

Study contacts

• Principal investigator: Kuniyuki Furuta — SOIK Corporation
• Study coordinator: Kuniyuki Furuta
• Email: furuta@soik.co.jp
• Phone: +818099740409

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

• Using infrared cameras to help detect juvenile arthritis in kids

  This study tests whether infrared thermography can help identify joint inflammation in children with juvenile idiopathic arthritis compared with standard tests.

  Recruiting Centre Hospitalier Universitaire, Amiens · Amiens · NCT07914179

• Automated Echocardiography Measurements Using Deep-Learning Software

  This study tests whether US2.AI can automatically measure heart structure and function from standard echocardiograms as accurately as expert clinicians in adults.

  Recruiting Centro Cardiologico Monzino · Milan, Lombardy · NCT07738419

• Timing of AI Help in Chest X-Rays: Does When AI Becomes Visible Change How Clinicians Read Images?

  Doctors test chest X-ray interpretation with AI information shown either before or after they start reviewing the image to see how timing affects their search patterns and decisions.

  Recruiting University Hospitals, Leicester · Leicester, Leicestershire · NCT07675694

• Nutrition and breastfeeding: following moms and babies in Arkansas

  This study will test how mothers’ diets and social factors like food access relate to breastfeeding outcomes and baby health, following participants from late pregnancy through the first year after birth.

  Recruiting Arkansas Children's Hospital Research Institute · Little Rock, Arkansas · NCT07477106

• Routine STI screening during pregnancy to improve birth outcomes

  This study tests whether adding screening and treatment for chlamydia and gonorrhoea to standard antenatal care can reduce preterm birth and other problems for pregnant women in Botswana.

  Recruiting University of California, San Diego · Gaborone · NCT07458802

• Deep-learning tool to distinguish T1–T2 from T3 renal cell carcinoma on contrast-enhanced CT.

  This project will see if a computer model can tell whether kidney cancer is early (T1–T2) or locally advanced (T3) using contrast-enhanced CT scans before surgery.

  Recruiting Peking University First Hospital · Beijing · NCT07166445

How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.