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AI Blind-Sweep Ultrasound for Antenatal Screening by Non-Specialist Health Workers in Rural DR Congo (FS2)

10 de septiembre de 2026 actualizado por: SOIK Corporation Sarl

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

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). Planned enrollment is approximately 1,380: Batch 1 (closed, 80 enrolled) and Batch 2 (approximately 1,300), within an IRB-approved ceiling of 3,000. Early termination is 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.

Descripción general del estudio

Estado

Reclutamiento

Condiciones

Intervención / Tratamiento

Descripción detallada

Study conduct is organised in Batches. Batch 1 (Centre de Sante CBCO, Kenge health zone, Kwango province) enrolled 80 participants, of whom 78 had complete datasets, and is closed. Batch 2 is planned at approximately 1,300 participants. The confirmatory analysis cut is approximately 130 to 160 cases acquired with a model frozen before collection, comprising a core set of 80 to 100, an early-gestation enrichment of 25 to 30, and an upper-gestational-age enrichment of 25 to 30. Enrolment may stop early once the pre-specified confidence-interval criteria for the two primary gestational-age outcomes are met. The registered enrolment ceiling of 3,000 is unchanged.

Sites are facilities of the Kenge health zone, Kwango province, within the perimeter already approved in the protocol (up to 6 facilities plus 2 to 5 village outreach sites).

Eligibility follows the protocol: pregnant women aged 18 years or older. Multiple pregnancies are eligible.

Two sub-cohorts address the bounds of the gestational-age range. The early sub-cohort (crown-rump length 7 to 84 mm, approximately under 14 weeks) draws on the same source population as Cohort B-2 and is recruited through community health worker (RECO) village outreach and neighbouring health facilities; it is reported descriptively. The upper-bound sub-cohort (35 weeks or more) is a separate validation subset, recruited in parallel and analysed separately; sensitivity and specificity are reported, and positive and negative predictive values are not reported because prevalence in this subset is artificial.

The registered Trimester 3 estimation window (28 to 36 weeks) is unchanged. In Batch 2, point estimates are reported for 28 to 34 weeks, and 35 to 36 weeks is handled as "to be confirmed / refer". This is an operational reporting restriction, not a change to the registered window.

The AI model is identified and frozen before each Batch and recorded in a model and application version registry under a pre-determined change control plan. Results are reported by frozen version. AI inference is not used for clinical decision-making.

Tipo de estudio

De observación

Inscripción (Estimado)

3000

Contactos y Ubicaciones

Esta sección proporciona los datos de contacto de quienes realizan el estudio e información sobre dónde se lleva a cabo este estudio.

Estudio Contacto

  • Nombre: Kuniyuki Furuta
  • Número de teléfono: +818099740409
  • Correo electrónico: furuta@soik.co.jp

Ubicaciones de estudio

Criterios de participación

Los investigadores buscan personas que se ajusten a una determinada descripción, denominada criterio de elegibilidad. Algunos ejemplos de estos criterios son el estado de salud general de una persona o tratamientos previos.

Criterio de elegibilidad

Edades elegibles para estudiar

  • Adulto
  • Adulto Mayor

Acepta Voluntarios Saludables

Sí

Método de muestreo

Muestra no probabilística

Población de estudio

Pregnant women attending routine antenatal care at participating facilities or identified through community health worker (RECO) village outreach in the Kenge health zone, Kwango province, DRC.

Descripción

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

Plan de estudios

Esta sección proporciona detalles del plan de estudio, incluido cómo está diseñado el estudio y qué mide el estudio.

¿Cómo está diseñado el estudio?

Detalles de diseño

Cohortes e Intervenciones

Grupo / Cohorte
Intervención / Tratamiento
Cohort A
Natural enrollment / Description: Consecutive ANC attendees (natural prevalence)
Smartphone-based 9-sweep obstetric ultrasound with AI estimation of gestational age, fetal presentation, and placenta location, operated by trained non-specialist health workers after 30-60 minutes of training. The AI model is frozen before each Batch and managed in a version registry under a pre-determined change control plan (PCCP); AI inference is not used for clinical decision-making.
Cohort B-1 Placenta/malpresentation enriched
Enriched validation subset for placenta praevia and fetal malpresentation
Smartphone-based 9-sweep obstetric ultrasound with AI estimation of gestational age, fetal presentation, and placenta location, operated by trained non-specialist health workers after 30-60 minutes of training. The AI model is frozen before each Batch and managed in a version registry under a pre-determined change control plan (PCCP); AI inference is not used for clinical decision-making.
Cohort B-2 - Trimester 1 (CRL/GS) enriched
Enriched trimester 1 cases for CRL/GS gestational age models
Smartphone-based 9-sweep obstetric ultrasound with AI estimation of gestational age, fetal presentation, and placenta location, operated by trained non-specialist health workers after 30-60 minutes of training. The AI model is frozen before each Batch and managed in a version registry under a pre-determined change control plan (PCCP); AI inference is not used for clinical decision-making.
Cohort B-3 - Early gestation (CRL 7-84 mm) enriched
Early-gestation enrichment sub-cohort: crown-rump length 7-84 mm, approximately under 14 weeks of gestation. Drawn from the same source population as Cohort B-2 and recruited through community health worker (RECO) village outreach and neighbouring health facilities. Reported descriptively.
Smartphone-based 9-sweep obstetric ultrasound with AI estimation of gestational age, fetal presentation, and placenta location, operated by trained non-specialist health workers after 30-60 minutes of training. The AI model is frozen before each Batch and managed in a version registry under a pre-determined change control plan (PCCP); AI inference is not used for clinical decision-making.
Cohort B-4 - Upper gestational-age bound (>=35 weeks) validation subset
Validation subset at the upper bound of the gestational-age range (35 weeks of gestation or more), recruited in parallel with the main cohort and analysed separately. Sensitivity and specificity are reported. Positive and negative predictive values are not reported, because prevalence in this subset is artificial.
Smartphone-based 9-sweep obstetric ultrasound with AI estimation of gestational age, fetal presentation, and placenta location, operated by trained non-specialist health workers after 30-60 minutes of training. The AI model is frozen before each Batch and managed in a version registry under a pre-determined change control plan (PCCP); AI inference is not used for clinical decision-making.

¿Qué mide el estudio?

Medidas de resultado primarias

Medida de resultado
Medida Descripción
Periodo de tiempo
Gestational age mean absolute error (MAE), Trimester 2 (14-27 weeks)
Periodo de tiempo: Through study completion, up to 12 months
MAE in days vs reference standard, with 95% CI; target MAE ≤7 days and upper 95% CI ≤10 days
Through study completion, up to 12 months
Gestational age mean absolute error (MAE), Trimester 3 (28-36 weeks)
Periodo de tiempo: Through study completion, up to 12 months
MAE in days vs reference standard, with 95% CI; target MAE ≤10 days and upper 95% CI ≤14 days
Through study completion, up to 12 months
AI confidence calibration - Expected Calibration Error (ECE)
Periodo de tiempo: At Batch 1 closure, up to 8 weeks
Expected Calibration Error from the reliability diagram of AI confidence versus reference-standard agreement; target <=0.05
At Batch 1 closure, up to 8 weeks
AI confidence calibration - Brier score
Periodo de tiempo: At Batch 1 closure, up to 8 weeks
Brier score of AI confidence versus reference-standard agreement; target <=0.20
At Batch 1 closure, up to 8 weeks

Medidas de resultado secundarias

Medida de resultado
Medida Descripción
Periodo de tiempo
Trimester 1 gestational age MAE (CRL and GS models)
Periodo de tiempo: Through study completion, up to 12 months
Integrated Cohort A T1 + Cohort B-2, N=30; CRL model MAE ≤5 days or GS model MAE ≤7 days
Through study completion, up to 12 months
Placenta praevia and fetal malpresentation sensitivity and specificity
Periodo de tiempo: Through study completion, up to 12 months
Observed sensitivity/specificity with 95% CI (Clopper-Pearson) on enriched validation subset (Cohort B-1, target N=20-25); reported as observed values
Through study completion, up to 12 months
Inter-rater reliability of gestational age - Intraclass Correlation Coefficient (ICC)
Periodo de tiempo: During the inter-rater assessment window, up to 1 week
ICC(2,1) between two independent readers for gestational age in days
During the inter-rater assessment window, up to 1 week
Inter-rater reliability of placenta praevia classification - Cohen's kappa
Periodo de tiempo: During the inter-rater assessment window, up to 1 week
Cohen's kappa for placenta praevia (yes/no) between two independent readers
During the inter-rater assessment window, up to 1 week
Inter-rater reliability of fetal presentation classification - weighted kappa
Periodo de tiempo: During the inter-rater assessment window, up to 1 week
Linear weighted kappa for fetal presentation category between two independent readers
During the inter-rater assessment window, up to 1 week
Acceptability - Acceptability of Intervention Measure (AIM) score
Periodo de tiempo: Through study completion, up to 12 months
Mean AIM score (4-item, 5-point Likert) among health workers
Through study completion, up to 12 months
Feasibility - ultrasound throughput (scans per device per day)
Periodo de tiempo: Through study completion, up to 12 months
Number of scans completed per device per day; target >=15
Through study completion, up to 12 months
Fidelity - protocol adherence rate (5-item checklist)
Periodo de tiempo: Through study completion, up to 12 months
Percentage adherence on a 5-item fidelity checklist; target >=80%
Through study completion, up to 12 months
Penetration - proportion of eligible women screened
Periodo de tiempo: Through study completion, up to 12 months
Percentage of eligible women in the catchment screened with AI ultrasound
Through study completion, up to 12 months
Sustainability - intention to continue use
Periodo de tiempo: At study completion, up to 12 months
Percentage of staff reporting intention to continue use at the end-of-study interview
At study completion, up to 12 months

Colaboradores e Investigadores

Aquí es donde encontrará personas y organizaciones involucradas en este estudio.

Patrocinador

Investigadores

  • Investigador principal: Kuniyuki Furuta, SOIK Corporation

Fechas de registro del estudio

Estas fechas rastrean el progreso del registro del estudio y los envíos de resultados resumidos a ClinicalTrials.gov. Los registros del estudio y los resultados informados son revisados ​​por la Biblioteca Nacional de Medicina (NLM) para asegurarse de que cumplan con los estándares de control de calidad específicos antes de publicarlos en el sitio web público.

Fechas importantes del estudio

Inicio del estudio (Actual)

19 de junio de 2026

Finalización primaria (Estimado)

31 de diciembre de 2026

Finalización del estudio (Estimado)

13 de junio de 2027

Fechas de registro del estudio

Enviado por primera vez

18 de junio de 2026

Primero enviado que cumplió con los criterios de control de calidad

24 de junio de 2026

Publicado por primera vez (Actual)

1 de julio de 2026

Actualizaciones de registros de estudio

Última actualización publicada (Actual)

15 de septiembre de 2026

Última actualización enviada que cumplió con los criterios de control de calidad

10 de septiembre de 2026

Última verificación

1 de septiembre de 2026

Más información

Términos relacionados con este estudio

Otros números de identificación del estudio

  • SOIK-FS2-2026

Plan de datos de participantes individuales (IPD)

¿Planea compartir datos de participantes individuales (IPD)?

INDECISO

Información sobre medicamentos y dispositivos, documentos del estudio

Estudia un producto farmacéutico regulado por la FDA de EE. UU.

No

Estudia un producto de dispositivo regulado por la FDA de EE. UU.

No

Esta información se obtuvo directamente del sitio web clinicaltrials.gov sin cambios. Si tiene alguna solicitud para cambiar, eliminar o actualizar los detalles de su estudio, comuníquese con register@clinicaltrials.gov. Tan pronto como se implemente un cambio en clinicaltrials.gov, también se actualizará automáticamente en nuestro sitio web. .