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
調査の概要
状態
状態
条件
条件
介入・治療
介入・治療
詳細な説明
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.
研究の種類
研究の種類
入学 (推定)
入学
連絡先と場所
研究連絡先
研究連絡先
- 名前:Kuniyuki Furuta
- 電話番号:+818099740409
- メール:furuta@soik.co.jp
研究場所
-
-
Kwango
-
Kenge、Kwango、コンゴ民主共和国
- 募集
- Centre de Sante CBCO
-
コンタクト:
- Henoch Bulu
- 電話番号:+243812268392
- メール:drhenochbulu@gmail.com
-
-
参加基準
適格基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
サンプリング方法
調査対象母集団
説明
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
研究計画
研究はどのように設計されていますか?
デザインの詳細
グループ/コホートの数
コホートと介入
グループ/コホートグループ/コホート |
介入・治療介入・治療 |
|---|---|
|
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.
|
この研究は何を測定していますか?
主要な結果の測定
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Gestational age mean absolute error (MAE), Trimester 2 (14-27 weeks)
時間枠: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
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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)
時間枠: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)
時間枠: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
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At Batch 1 closure, up to 8 weeks
|
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AI confidence calibration - Brier score
時間枠: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
|
二次結果の測定
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Trimester 1 gestational age MAE (CRL and GS models)
時間枠: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
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Through study completion, up to 12 months
|
|
Placenta praevia and fetal malpresentation sensitivity and specificity
時間枠: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)
時間枠: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
|
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Inter-rater reliability of placenta praevia classification - Cohen's kappa
時間枠: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
時間枠: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
|
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Acceptability - Acceptability of Intervention Measure (AIM) score
時間枠:Through study completion, up to 12 months
|
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)
時間枠: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)
時間枠: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
時間枠: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
時間枠: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
|
協力者と研究者
捜査官
捜査官
- 主任研究者:Kuniyuki Furuta、SOIK Corporation
研究記録日
主要日程の研究
研究開始 (実際)
研究開始
一次修了 (推定)
一次修了
研究の完了 (推定)
研究の完了
試験登録日
最初に提出
最初に提出
QC基準を満たした最初の提出物
QC基準を満たした最初の提出物
最初の投稿 (実際)
最初の投稿
学習記録の更新
投稿された最後の更新 (実際)
投稿された最後の更新
QC基準を満たした最後の更新が送信されました
QC基準を満たした最後の更新が送信されました
最終確認日
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
その他の研究ID番号
- SOIK-FS2-2026
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
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