Early Diagnosis and Prediction of Maternal and Neonatal Diseases: (EDPMND)

Early Prediction and Diagnosis of Maternal and Neonatal Diseases Using Multimodal Health Data

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.

Study Overview

• Status: Recruiting
• Conditions: Pregnancy-Related and Neonatal Disorders
• Intervention / Treatment: Diagnostic test: AI-Based Diagnostic and Prognostic Model

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 Type: Observational
• Enrollment (Estimated): 1000000

Contacts and Locations

• Study Contact: Name: Fei Liu, MD; Phone Number: +86 13810512704; Email: liufei_2359@163.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China
      • Recruiting
      • Guangzhou Women And Children's Medical Center
      • Contact: Bingzhou Liu, MD; Phone Number: +86-0756-2222569; Email: mr_jerry_99@163.com
  • Zhejiang
    • Wenzhou, Zhejiang, China
      • Recruiting
      • First Affiliated Hospital of Wenzhou Medical University
      • Contact: Cheng Tang, MD; Phone Number: +86-0577-55579999; Email: c249325687@163.com
    • Wenzhou, Zhejiang, China
      • Recruiting
      • Second Affiliated Hospital of Wenzhou Medical University
      • Contact: Sian Liu, MD; Phone Number: +86-0577-88002888; Email: liusan@mail3.sysu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: Yes

• 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.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; 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.
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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sensitivity (True Positive Rate)	Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders.	1 year
Specificity (True Negative Rate)	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.	1 year

Collaborators and Investigators

• Sponsor: The Eye Hospital of Wenzhou Medical University

Study record dates

Study Major Dates

• Study Start (Actual): August 1, 2023
• Primary Completion (Estimated): May 1, 2025
• Study Completion (Estimated): May 1, 2025

Study Registration Dates

• First Submitted: January 19, 2025
• First Submitted That Met QC Criteria: January 19, 2025
• First Posted (Actual): January 24, 2025

Study Record Updates

• Last Update Posted (Actual): April 17, 2025
• Last Update Submitted That Met QC Criteria: April 16, 2025
• Last Verified: April 1, 2025

More Information

Terms related to this study

• Keywords: AI-Assisted Diagnosis; Early Disease Prediction; Maternal and Neonatal Health
• Additional Relevant MeSH Terms: Infant, Newborn, Diseases
• Other Study ID Numbers: Maternal and Neonatal Diseases

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No