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

10 сентября 2026 г. обновлено: 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.

Обзор исследования

Статус

Рекрутинг

Условия

Вмешательство/лечение

Подробное описание

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.

Тип исследования

Наблюдательный

Регистрация (Оцененный)

3000

Контакты и местонахождение

В этом разделе приведены контактные данные лиц, проводящих исследование, и информация о том, где проводится это исследование.

Контакты исследования

  • Имя: Kuniyuki Furuta
  • Номер телефона: +818099740409
  • Электронная почта: furuta@soik.co.jp

Места учебы

Критерии участия

Исследователи ищут людей, которые соответствуют определенному описанию, называемому критериям приемлемости. Некоторыми примерами этих критериев являются общее состояние здоровья человека или предшествующее лечение.

Критерии приемлемости

Возраст, подходящий для обучения

  • Взрослый
  • Пожилой взрослый

Принимает здоровых добровольцев

Да

Метод выборки

Невероятностная выборка

Исследуемая популяция

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.

Описание

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

Учебный план

В этом разделе представлена ​​подробная информация о плане исследования, в том числе о том, как планируется исследование и что оно измеряет.

Как устроено исследование?

Детали дизайна

Когорты и вмешательства

Группа / когорта
Вмешательство/лечение
Cohort A
Natural enrollment / Description: Consecutive ANC attendees (natural prevalence)
Диагностический тест: 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
Диагностический тест: 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
Диагностический тест: 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.
Диагностический тест: 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.
Диагностический тест: 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.

Что измеряет исследование?

Первичные показатели результатов

Мера результата
Мера Описание
Временное ограничение
Gestational age mean absolute error (MAE), Trimester 2 (14-27 weeks)
Временное ограничение: 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)
Временное ограничение: 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)
Временное ограничение: 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
Временное ограничение: 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

Вторичные показатели результатов

Мера результата
Мера Описание
Временное ограничение
Trimester 1 gestational age MAE (CRL and GS models)
Временное ограничение: 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
Временное ограничение: 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)
Временное ограничение: 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
Временное ограничение: 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
Временное ограничение: 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
Временное ограничение: 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)
Временное ограничение: 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)
Временное ограничение: 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
Временное ограничение: 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
Временное ограничение: 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

Соавторы и исследователи

Здесь вы найдете людей и организации, участвующие в этом исследовании.

Спонсор

SOIK Corporation Sarl

Следователи

  • Главный следователь: Kuniyuki Furuta, SOIK Corporation

Даты записи исследования

Эти даты отслеживают ход отправки отчетов об исследованиях и сводных результатов на сайт ClinicalTrials.gov. Записи исследований и сообщаемые результаты проверяются Национальной медицинской библиотекой (NLM), чтобы убедиться, что они соответствуют определенным стандартам контроля качества, прежде чем публиковать их на общедоступном веб-сайте.

Изучение основных дат

Начало исследования (Действительный)

19 июня 2026 г.

Первичное завершение (Оцененный)

31 декабря 2026 г.

Завершение исследования (Оцененный)

13 июня 2027 г.

Даты регистрации исследования

Первый отправленный

18 июня 2026 г.

Впервые представлено, что соответствует критериям контроля качества

24 июня 2026 г.

Первый опубликованный (Действительный)

1 июля 2026 г.

Обновления учебных записей

Последнее опубликованное обновление (Действительный)

15 сентября 2026 г.

Последнее отправленное обновление, отвечающее критериям контроля качества

10 сентября 2026 г.

Последняя проверка

1 сентября 2026 г.

Дополнительная информация

Термины, связанные с этим исследованием

Ключевые слова

Дополнительные соответствующие термины MeSH

Другие идентификационные номера исследования

  • SOIK-FS2-2026

Планирование данных отдельных участников (IPD)

Планируете делиться данными об отдельных участниках (IPD)?

НЕ РЕШЕНО

Информация о лекарствах и устройствах, исследовательские документы

Изучает лекарственный продукт, регулируемый FDA США.

Нет

Изучает продукт устройства, регулируемый Управлением по санитарному надзору за качеством пищевых продуктов и медикаментов США.

Нет

Эта информация была получена непосредственно с веб-сайта clinicaltrials.gov без каких-либо изменений. Если у вас есть запросы на изменение, удаление или обновление сведений об исследовании, обращайтесь по адресу register@clinicaltrials.gov. Как только изменение будет реализовано на clinicaltrials.gov, оно будет автоматически обновлено и на нашем веб-сайте. .

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