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

10 settembre 2026 aggiornato da: 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.

Panoramica dello studio

Stato

Reclutamento

Condizioni

Intervento / Trattamento

Descrizione dettagliata

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 di studio

Osservativo

Iscrizione (Stimato)

3000

Contatti e Sedi

Questa sezione fornisce i recapiti di coloro che conducono lo studio e informazioni su dove viene condotto lo studio.

Contatto studio

Luoghi di studio

Criteri di partecipazione

I ricercatori cercano persone che corrispondano a una certa descrizione, chiamata criteri di ammissibilità. Alcuni esempi di questi criteri sono le condizioni generali di salute di una persona o trattamenti precedenti.

Criteri di ammissibilità

Età idonea allo studio

  • Adulto
  • Adulto più anziano

Accetta volontari sani

Sì

Metodo di campionamento

Campione non probabilistico

Popolazione di studio

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.

Descrizione

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

Piano di studio

Questa sezione fornisce i dettagli del piano di studio, compreso il modo in cui lo studio è progettato e ciò che lo studio sta misurando.

Come è strutturato lo studio?

Dettagli di progettazione

Coorti e interventi

Gruppo / Coorte
Intervento / Trattamento
Cohort A
Natural enrollment / Description: Consecutive ANC attendees (natural prevalence)
Test diagnostico: SPAQ E-con AI blind-sweep ultrasound
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
Test diagnostico: SPAQ E-con AI blind-sweep ultrasound
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
Test diagnostico: SPAQ E-con AI blind-sweep ultrasound
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.
Test diagnostico: SPAQ E-con AI blind-sweep ultrasound
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.
Test diagnostico: SPAQ E-con AI blind-sweep ultrasound
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.

Cosa sta misurando lo studio?

Misure di risultato primarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Gestational age mean absolute error (MAE), Trimester 2 (14-27 weeks)
Lasso di tempo: 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)
Lasso di tempo: 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)
Lasso di tempo: 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
Lasso di tempo: 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

Misure di risultato secondarie

Misura del risultato
Misura Descrizione
Lasso di tempo
Trimester 1 gestational age MAE (CRL and GS models)
Lasso di tempo: 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
Lasso di tempo: 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)
Lasso di tempo: 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
Lasso di tempo: 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
Lasso di tempo: 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
Lasso di tempo: 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)
Lasso di tempo: 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)
Lasso di tempo: 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
Lasso di tempo: 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
Lasso di tempo: 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

Collaboratori e investigatori

Qui è dove troverai le persone e le organizzazioni coinvolte in questo studio.

Sponsor

SOIK Corporation Sarl

Investigatori

  • Investigatore principale: Kuniyuki Furuta, SOIK Corporation

Studiare le date dei record

Queste date tengono traccia dell'avanzamento della registrazione dello studio e dell'invio dei risultati di sintesi a ClinicalTrials.gov. I record degli studi e i risultati riportati vengono esaminati dalla National Library of Medicine (NLM) per assicurarsi che soddisfino specifici standard di controllo della qualità prima di essere pubblicati sul sito Web pubblico.

Studia le date principali

Inizio studio (Effettivo)

19 giugno 2026

Completamento primario (Stimato)

31 dicembre 2026

Completamento dello studio (Stimato)

13 giugno 2027

Date di iscrizione allo studio

Primo inviato

18 giugno 2026

Primo inviato che soddisfa i criteri di controllo qualità

24 giugno 2026

Primo Inserito (Effettivo)

1 luglio 2026

Aggiornamenti dei record di studio

Ultimo aggiornamento pubblicato (Effettivo)

15 settembre 2026

Ultimo aggiornamento inviato che soddisfa i criteri QC

10 settembre 2026

Ultimo verificato

1 settembre 2026

Maggiori informazioni

Termini relativi a questo studio

Parole chiave

Termini MeSH pertinenti aggiuntivi

Altri numeri di identificazione dello studio

  • SOIK-FS2-2026

Piano per i dati dei singoli partecipanti (IPD)

Hai intenzione di condividere i dati dei singoli partecipanti (IPD)?

INDECISO

Informazioni su farmaci e dispositivi, documenti di studio

Studia un prodotto farmaceutico regolamentato dalla FDA degli Stati Uniti

No

Studia un dispositivo regolamentato dalla FDA degli Stati Uniti

No

Queste informazioni sono state recuperate direttamente dal sito web clinicaltrials.gov senza alcuna modifica. In caso di richieste di modifica, rimozione o aggiornamento dei dettagli dello studio, contattare register@clinicaltrials.gov. Non appena verrà implementata una modifica su clinicaltrials.gov, questa verrà aggiornata automaticamente anche sul nostro sito web .

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