RecruitingNCT07677670

Diagnostic Accuracy and Implementation Feasibility of AI-Assisted Blind Ultrasound Sweep (SPAQ E-con AI) for Antenatal Screening by Non-Specialist Health Workers in Rural Democratic Republic of the Congo

Sponsor: SOIK Corporation Sarl

Trial Details

Condition	Antenatal Care, Maternal Health, Gestational Age, Placenta Praevia, Diagnostic Imaging
Intervention	SPAQ E-con AI blind-sweep ultrasound
Status	Recruiting
Participants	3,000
Est. Completion	31 Dec 2026
Last Updated	1 July 2026

Primary Outcome

Gestational age mean absolute error (MAE), Trimester 2 (14-27 weeks); Gestational age mean absolute error (MAE), Trimester 3 (28-36 weeks); AI confidence calibration - Expected Calibration Error (ECE); AI confidence calibration - Brier score

Eligibility Criteria

Inclusion 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

Trial Sites

Countries

Democratic Republic of the Congo

View Registry Entry

Related Analysis

Prospective Trial Opens: AI-Assisted Blind-Sweep Ultrasound (SPAQ E-con AI) vs Reference Standard for Antenatal Screening by Non-Specialist Workers in Rural DRC — Implications for Global Maternal Health

A prospective multi-centre trial (NCT07677670) is now recruiting in rural Democratic Republic of the Congo to evaluate whether AI-assisted blind-sweep ultrasound (SPAQ E-con AI), operated by trained non-specialist health workers, can accurately estimate gestational age and support antenatal screening without a sonographer present. With a target enrolment of approximately 1,430 women and completion expected by end of 2026, the findings could reshape task-shifted obstetric care delivery across low-resource settings globally.

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