Early Diagnosis and Prediction of Maternal and Neonatal Diseases:

Recruiting

• Conditions: Pregnancy-Related and Neonatal Disorders

• Registration Number: NCT06791343
• Lead Sponsor: The Eye Hospital of Wenzhou Medical University

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.

Detailed Description

Maternal and neonatal health significantly impact the well-being of both mothers and infants. Early screening, diagnosis, and intervention are crucial for preventing the onset and progression of pregnancy-related diseases and neonatal conditions. In clinical practice, obstetricians and pediatricians often need to integrate a wide range of patient data, including demographic information, medical history, biochemical markers such as blood glucose and lipid levels, as well as various imaging data such as ultrasounds, fetal monitoring, and laboratory test results, to make an accurate diagnosis and develop an appropriate care plan. In an era where precision and personalized medicine are at the forefront of healthcare, the early detection and diagnosis of maternal and neonatal diseases, as well as the selection of suitable diagnostic and therapeutic strategies, have become significant challenges in clinical settings. Recent advancements in medical imaging and data analysis techniques have greatly enhanced the accuracy and effectiveness of maternal and neonatal disease diagnosis. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic medical records, imaging, and laboratory results, in combination with deep learning techniques. The objective is to improve diagnostic accuracy, streamline clinical workflows, and provide more personalized care options for mothers and infants. Ultimately, this system seeks to enhance health outcomes and improve the overall quality of life for both mothers and their newborns.

Study Timeline

• Study Start: 2023-08-01
• Study First Submitted: 2025-01-19
• Study First Submitted QC: 2025-01-19
• Study First Posted: 2025-01-24
• Status Verified: 2025-04
• Last Update Submitted: 2025-04-16
• Last Update Posted: 2025-04-17
• Primary Completion: 2025-05
• Study Completion: 2025-05

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1000000

Inclusion Criteria

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

Exclusion Criteria

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Area Under the Curve (AUC)	1 year	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).
F1 Score	1 year	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.

Secondary Outcome Measures

Name	Time	Method
Sensitivity (True Positive Rate)	1 year	Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders.
Specificity (True Negative Rate)	1 year	Specificity measures the ability of the AI model to correctly identify cases without diseases, ensuring that healthy mothers and infants are correctly identified as negative.

Trial Locations

Locations (3)

Guangzhou Women and Children's Medical Center; 🇨🇳 Guangzhou, Guangdong, China

First Affiliated Hospital of Wenzhou Medical University; 🇨🇳 Wenzhou, Zhejiang, China

Second Affiliated Hospital of Wenzhou Medical University; 🇨🇳 Wenzhou, Zhejiang, China