Early Diagnosis and Prediction of Maternal and Neonatal Diseases:

NCT06791343 | Recruiting

• 1 other identifier: Maternal and Neonatal Diseases
• Study Type: observational
• Target: 1,000,000
• Locations: 1 country
• Sites: 3

Brief Summary

This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying maternal and neonatal diseases, leveraging multimodal health data.

Conditions

Pregnancy-Related and Neonatal Disorders

Keywords

Maternal and Neonatal Health; Early Disease Prediction; AI-Assisted Diagnosis

Outcome Measures

Primary Outcomes (2)

• Area Under the Curve (AUC)

  AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).

  1 year

• F1 Score

  The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.

  1 year

Secondary Outcomes (2)

• Sensitivity (True Positive Rate)

  1 year

• Specificity (True Negative Rate)

  1 year

Study Arms (2)

Healthy Maternal and Neonatal Cohort

This group consists of pregnant mothers with no pregnancy-related diseases and their healthy newborns. Participants in this cohort will serve as the control group for comparison to the experimental group. No interventions or treatments will be administered to this cohort, as they represent the baseline of healthy pregnancies and newborns.

Diagnostic Test: AI-Based Diagnostic and Prognostic Model

Maternal and Neonatal Disease Cohort

This group consists of pregnant mothers who have been diagnosed with pregnancy-related diseases or their affected newborns. Participants in this cohort will serve as the experimental group for evaluating the effectiveness of the early prediction model in identifying maternal and neonatal health risks.

Diagnostic Test: AI-Based Diagnostic and Prognostic Model

Interventions

AI-Based Diagnostic and Prognostic Model DIAGNOSTIC_TEST

This intervention involves an AI system that integrates multimodal data, including maternal health records, laboratory test results, and imaging data, to predict the risk of maternal and neonatal diseases. The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of health complications. By analyzing historical health data, the model aims to predict potential risks for both mothers and infants, improving early intervention and outcomes.

Healthy Maternal and Neonatal Cohort; Maternal and Neonatal Disease Cohort

Eligibility Criteria

• Age: 18 Years - 45 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of pregnant women aged 18 to 45 years who have received care at participating study centers. Participants must have comprehensive electronic health records (EHRs) available, including prenatal care data, laboratory results, or imaging data. Both healthy mothers and those with pregnancy-related diseases (e.g., preeclampsia, gestational diabetes) will be included in the study to assess the AI-assisted model's diagnostic capabilities. The study will focus on patients with documented care records from the participating centers.

You may qualify if:

• Pregnant women aged 18 to 45 years.
• Women who have received prenatal care at participating centers (e.g., hospitals or clinics).
• Availability of comprehensive electronic health records, including prenatal care data, laboratory results, and imaging records.
• Willingness to provide consent for participation in the study and the use of historical health data for analysis.

You may not qualify if:

• Women under 18 or over 45 years old.
• Participants with insufficient follow-up data or missing critical clinical information required for predictive modeling.

Sponsors & Collaborators

• The Eye Hospital of Wenzhou Medical University: lead

Study Sites (3)

Guangzhou Women and Children's Medical Center

Guangzhou, Guangdong, China

RECRUITING

First Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

RECRUITING

Second Affiliated Hospital of Wenzhou Medical University

Wenzhou, Zhejiang, China

RECRUITING

MeSH Terms

Conditions

Infant, Newborn, Diseases

Condition Hierarchy (Ancestors)

Congenital, Hereditary, and Neonatal Diseases and Abnormalities

Central Study Contacts

Fei Liu, MD

CONTACT

+86 13810512704; liufei_2359@163.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief Scientist

Study Record Dates

• First Submitted: January 19, 2025
• First Posted: January 24, 2025
• Study Start: August 1, 2023
• Primary Completion: May 1, 2025
• Study Completion: May 1, 2025
• Last Updated: April 17, 2025

Record last verified: 2025-04

Data Sharing

• IPD Sharing: Will not share

Locations

CN (3)