Integration of clinical trial data within an AI-IoT architecture for predictive stillbirth risk and Materiovigilance.
| Study/Trial name | Clinical trial ID | Study type | IoT/Sensing technology | AI/Predictive modelling approach | Gestational age range | Primary outcomes | Relevance to Materiovigilance & stillbirth risk | References |
|---|---|---|---|---|---|---|---|---|
| Pregnancy, Preterm Birth & Stillbirth Outcomes Study | NCT02738892 | Observational cohort | Hospital EHR, fetal monitoring systems | Statistical risk modelling, multivariate analysis | All trimesters | Stillbirth, preterm birth | Foundational dataset for AI-based stillbirth risk models | [61, 62] |
| Modified Biophysical Profile | NCT03729089 | Interventional | Ultrasound, CTG sensors | Threshold-based + predictive analytics | ≥ 28 weeks | Adverse fetal outcomes | Enhances sensor-driven risk stratification | [63] |
| In-Home Non-Stress Fetal Monitoring Device | NCT07223996 | Interventional (device) | Home-based IoT FHR monitor | Signal processing, trend analysis | 28–40 weeks | Device safety, fetal well-being | Core Materiovigilance study for remote IoT devices | [64] |
| AI-Powered CURA™ Risk Screening App | NCT06974188 | Interventional (AI) | Mobile health data, EHR integration | Machine learning risk scoring | All trimesters | Identification of high-risk pregnancy | Demonstrates clinical deployment of AI decision support | [65] |
| Remote Telemedicine Fetal Monitoring Study | NCT06366711 | Feasibility | Wearable IoT sensors, cloud platform | Time-series analytics | Late second–third trimester | Monitoring feasibility | Supports continuous surveillance and early warning systems | [66] |
| Monica Novii Wireless Patch System | NCT03223324 | Interventional (device) | Wearable fetal ECG IoT patch | Signal quality algorithms | ≥ 32 weeks | FHR accuracy and safety | Materiovigilance of wireless fetal monitoring devices | [67] |
| Umbilical Cord Abnormalities Study | NCT05901688 | Observational | Ultrasound imaging systems | Regression-based risk modeling | Mid–late pregnancy | Adverse fetal outcomes | Contributes anatomical predictors for AI models | [68] |
| PRISMA Maternal & Newborn Health Study | NCT05904145 | Observational | Integrated clinical data platforms | Predictive analytics | All trimesters | Fetal and maternal mortality | Population-level surveillance for stillbirth trends | [69] |
AI: artificial intelligence; CTG: cardiotocography; ECG: electrocardiography; IoT: Internet of Things; FHR: fetal heart rate.
The supplementary tables for this article are available at: https://www.explorationpub.com/uploads/Article/file/1001399_sup_1.xlsx.
Authors (Atreyi Pramanik and Pardeep Yadav) are thankful to Ms. Shivani Singh, Ph.D. Scholar of the Uttaranchal University C/o Dr. Pardeep Yadav, for her assistance during the extraction of the gene’s information, presented in the supplementary files. During the preparation of this work, the author(s) used ChatGPT in order to improve the grammar and syntax. After using the tool/service, author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.
AP: Writing—original draft, Formal analysis, Data curation. PY: Conceptualization, Methodology, Writing—review & editing, Supervision. SKJ: Investigation, Visualization, Validation. SPP: Resources, Software, Data curation. PG: Conceptualization, Supervision, Project administration, Writing—review & editing. All authors read and approved the submitted version.
The authors declare that there are no conflicts of interest.
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The supplementary datasets and figures can be found in the supplementary files.
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