AI Blind-Sweep Ultrasound for Antenatal Screening by Non-Specialist Health Workers in Rural DR Congo (FS2)
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
연구 개요
상태
상태
정황
정황
개입 / 치료
개입 / 치료
상세 설명
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.
연구 유형
연구 유형
등록 (추정된)
등록
연락처 및 위치
연구 연락처
연구 연락처
- 이름: Kuniyuki Furuta
- 전화번호: +818099740409
- 이메일: furuta@soik.co.jp
연구 장소
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Kwango
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Kenge, Kwango, 콩고 민주 공화국
- 모병
- Centre de Sante CBCO
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연락하다:
- Henoch Bulu
- 전화번호: +243812268392
- 이메일: drhenochbulu@gmail.com
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-
참여기준
자격 기준
자격 기준
공부할 수 있는 나이
- 성인
- 고령자
건강한 자원 봉사자를 받아들입니다
샘플링 방법
연구 인구
설명
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
공부 계획
연구는 어떻게 설계됩니까?
디자인 세부사항
그룹/코호트 수
코호트 및 개입
그룹/코호트그룹/코호트 |
개입 / 치료개입 / 치료 |
|---|---|
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Cohort A
Natural enrollment / Description: Consecutive ANC attendees (natural prevalence)
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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.
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Cohort B-1 Placenta/malpresentation enriched
Enriched validation subset for placenta praevia and fetal malpresentation
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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.
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Cohort B-2 - Trimester 1 (CRL/GS) enriched
Enriched trimester 1 cases for CRL/GS gestational age models
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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.
|
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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.
|
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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.
|
연구는 무엇을 측정합니까?
주요 결과 측정
주요 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Gestational age mean absolute error (MAE), Trimester 2 (14-27 weeks)
기간: Through study completion, up to 12 months
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MAE in days vs reference standard, with 95% CI; target MAE ≤7 days and upper 95% CI ≤10 days
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Through study completion, up to 12 months
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Gestational age mean absolute error (MAE), Trimester 3 (28-36 weeks)
기간: Through study completion, up to 12 months
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MAE in days vs reference standard, with 95% CI; target MAE ≤10 days and upper 95% CI ≤14 days
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Through study completion, up to 12 months
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AI confidence calibration - Expected Calibration Error (ECE)
기간: At Batch 1 closure, up to 8 weeks
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Expected Calibration Error from the reliability diagram of AI confidence versus reference-standard agreement; target <=0.05
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At Batch 1 closure, up to 8 weeks
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AI confidence calibration - Brier score
기간: At Batch 1 closure, up to 8 weeks
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Brier score of AI confidence versus reference-standard agreement; target <=0.20
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At Batch 1 closure, up to 8 weeks
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2차 결과 측정
2차 결과 측정
결과 측정 |
측정값 설명 |
기간 |
|---|---|---|
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Trimester 1 gestational age MAE (CRL and GS models)
기간: Through study completion, up to 12 months
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Integrated Cohort A T1 + Cohort B-2, N=30; CRL model MAE ≤5 days or GS model MAE ≤7 days
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Through study completion, up to 12 months
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Placenta praevia and fetal malpresentation sensitivity and specificity
기간: Through study completion, up to 12 months
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Observed sensitivity/specificity with 95% CI (Clopper-Pearson) on enriched validation subset (Cohort B-1, target N=20-25); reported as observed values
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Through study completion, up to 12 months
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Inter-rater reliability of gestational age - Intraclass Correlation Coefficient (ICC)
기간: During the inter-rater assessment window, up to 1 week
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ICC(2,1) between two independent readers for gestational age in days
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During the inter-rater assessment window, up to 1 week
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Inter-rater reliability of placenta praevia classification - Cohen's kappa
기간: During the inter-rater assessment window, up to 1 week
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Cohen's kappa for placenta praevia (yes/no) between two independent readers
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During the inter-rater assessment window, up to 1 week
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Inter-rater reliability of fetal presentation classification - weighted kappa
기간: During the inter-rater assessment window, up to 1 week
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Linear weighted kappa for fetal presentation category between two independent readers
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During the inter-rater assessment window, up to 1 week
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Acceptability - Acceptability of Intervention Measure (AIM) score
기간: Through study completion, up to 12 months
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Mean AIM score (4-item, 5-point Likert) among health workers
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Through study completion, up to 12 months
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Feasibility - ultrasound throughput (scans per device per day)
기간: Through study completion, up to 12 months
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Number of scans completed per device per day; target >=15
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Through study completion, up to 12 months
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Fidelity - protocol adherence rate (5-item checklist)
기간: Through study completion, up to 12 months
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Percentage adherence on a 5-item fidelity checklist; target >=80%
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Through study completion, up to 12 months
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Penetration - proportion of eligible women screened
기간: Through study completion, up to 12 months
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Percentage of eligible women in the catchment screened with AI ultrasound
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Through study completion, up to 12 months
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Sustainability - intention to continue use
기간: At study completion, up to 12 months
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Percentage of staff reporting intention to continue use at the end-of-study interview
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At study completion, up to 12 months
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공동 작업자 및 조사자
수사관
수사관
- 수석 연구원: Kuniyuki Furuta, SOIK Corporation
연구 기록 날짜
연구 주요 날짜
연구 시작 (실제)
연구 시작
기본 완료 (추정된)
기본 완료
연구 완료 (추정된)
연구 완료
연구 등록 날짜
최초 제출
최초 제출
QC 기준을 충족하는 최초 제출
QC 기준을 충족하는 최초 제출
처음 게시됨 (실제)
처음 게시됨
연구 기록 업데이트
마지막 업데이트 게시됨 (실제)
마지막 업데이트 게시됨
QC 기준을 충족하는 마지막 업데이트 제출
QC 기준을 충족하는 마지막 업데이트 제출
마지막으로 확인됨
마지막으로 확인됨
추가 정보
이 연구와 관련된 용어
기타 연구 ID 번호
기타 연구 ID 번호
- SOIK-FS2-2026
개별 참가자 데이터(IPD) 계획
개별 참가자 데이터(IPD)를 공유할 계획입니까?
약물 및 장치 정보, 연구 문서
미국 FDA 규제 의약품 연구
미국 FDA 규제 기기 제품 연구
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