Medical-AI trial: AI-assisted "blind-sweep" obstetric ultrasound by non-specialists — antenatal screening in rural DR Congo (SPAQ E-con AI) (NCT07677670)

In antenatal care, ultrasound is central to knowing gestational age and the condition of the fetus and placenta, but acquiring and reading it requires trained specialists. Where specialists and equipment are scarce, the screening itself becomes hard to access. This is where the blind-sweep method draws interest: a non-specialist moves the probe in a set pattern to collect images, and AI handles the interpretation. Because a specialist does not need to aim the scan, the pool of operators can widen.

The SPAQ E-con AI that this study evaluates supports exactly this non-specialist blind-sweep obstetric ultrasound with AI. Per the registry summary, the study is set in rural Democratic Republic of the Congo, where trained non-specialist health workers perform the scans, and it evaluates diagnostic accuracy and implementation feasibility.

Primary outcomes are gestational-age mean absolute error (trimester 2, 14–27 weeks; trimester 3, 28–36 weeks) and the calibration of the AI confidence (expected calibration error and Brier score). The accuracy reference is manual measurement by a reference reader (early ultrasound preferred; manual BPD, biparietal diameter, if unavailable; last menstrual period is not used).

It is multicenter and prospective, with a Hybrid Type 1 design that examines effectiveness and field implementation together, and it follows FDA PCCP (a pre-specified post-market change plan) and STARD-AI guidance, including a pre-specified model-update (a Batch 2 cut). The target is about 1,430 (IRB-approved ceiling 3,000), with early termination allowed once pre-specified analysis thresholds are met.

The key of this trial is that it evaluates not only AI accuracy but also whether non-specialists can operate it in the field (implementation feasibility) within the same framework. Setting AI confidence calibration as a primary outcome, and following regulatory and reporting frameworks such as FDA PCCP and STARD-AI, embodies a modern discipline for using medical AI responsibly in the real world.

As an effort to bring obstetric ultrasound to areas short of specialists it is significant, but this study is an accuracy and feasibility evaluation and does not establish effectiveness or replacement of specialists.