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Recruiting NCT07677670

NCT07677670 AI Blind-Sweep Ultrasound for Antenatal Screening by Non-Specialist Health Workers in Rural DR Congo

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Clinical Trial Summary
NCT ID NCT07677670
Status Recruiting
Phase
Sponsor SOIK Corporation Sarl
Condition Antenatal Care
Study Type OBSERVATIONAL
Enrollment 3,000 participants
Start Date 2026-06-19
Primary Completion 2026-12-31

Eligibility & Interventions

Sex Female only
Min Age 16 Years
Max Age N/A
Study Type OBSERVATIONAL
Interventions
SPAQ E-con AI blind-sweep ultrasound

Eligibility Fast-Check

Enter your details for a quick preliminary check. This does not replace medical advice.

What to Expect as a Participant

This is an observational study. You will not receive an experimental treatment; researchers will collect data based on your existing condition or standard treatment.

This trial targets 3,000 participants in total. It began in 2026-06-19 with a primary completion date of 2026-12-31.

⚠ This information is for research awareness only. Always consult your physician before joining any clinical trial. Participation is voluntary and you may withdraw at any time.

Brief Summary

FS2 evaluates the diagnostic accuracy and implementation feasibility of an AI-assisted blind-sweep obstetric ultrasound (SPAQ E-con AI), operated by trained non-specialist health workers, for antenatal screening in rural Democratic Republic of the Congo. Primary outcomes are gestational age mean absolute error (Trimester 2 and Trimester 3) with 95% confidence intervals and AI confidence calibration. The reference standard is manual measurement by a reference reader (early ultrasound first; manual BPD if unavailable; last menstrual period is not used). Target enrollment is approximately 1,430 (IRB-approved ceiling 3,000), with early termination permitted upon achievement of pre-specified analysis-plan thresholds. The study is a multi-center prospective Hybrid Type 1 Effectiveness-Implementation design and includes a pre-specified adaptive model-update (Batch 2 cut) plan following FDA PCCP and STARD-AI guidance.

Eligibility Criteria

Inclusion Criteria: * Pregnant women * Identified at a participating facility routine ANC visit or RECO village outreach within the Kenge health zone catchment * Informed consent obtained (16-17 years with parent/guardian consent) Exclusion Criteria: * Emergency presentation * Multiple pregnancy * Duplicate re-registration of an already-enrolled woman * Lacking capacity to consent * Planned relocation outside Kwango province during the study period * Refusal of consent

Contact & Investigator

Central Contact

Kuniyuki Furuta

✉ furuta@soik.co.jp

📞 +818099740409

Principal Investigator

Kuniyuki Furuta

PRINCIPAL INVESTIGATOR

SOIK Corporation

Frequently Asked Questions

Who can join the NCT07677670 clinical trial?

This trial is open to female participants only, aged 16 Years or older, studying Antenatal Care. Full inclusion and exclusion criteria are listed in the Eligibility Criteria section. Always confirm your eligibility with the research team before applying.

Is NCT07677670 currently recruiting?

Yes, NCT07677670 is actively recruiting participants. Contact the research team at furuta@soik.co.jp for enrollment information.

Where is the NCT07677670 trial being conducted?

This trial is being conducted at Kenge, Democratic Republic of the Congo.

Who is sponsoring the NCT07677670 clinical trial?

NCT07677670 is sponsored by SOIK Corporation Sarl. The principal investigator is Kuniyuki Furuta at SOIK Corporation. The trial plans to enroll 3,000 participants.

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ClinicalMetric — Independent clinical trial intelligence platform. Not affiliated with NIH, ClinicalTrials.gov, the U.S. FDA, or any pharmaceutical company, hospital, or clinical research organization. Trial data is sourced from ClinicalTrials.gov for informational purposes only and does not constitute medical advice. Do not make any treatment, enrollment, or health decisions based solely on information found here — always consult a qualified healthcare professional. Full Disclaimer  ·  Last Reviewed: April 2026  ·  Data Methodology