Clinical trial NCT06791343: Early Diagnosis and Prediction of Maternal and Neonatal Diseases:
Official title: Early Prediction and Diagnosis of Maternal and Neonatal Diseases Using Multimodal Health Data
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Pregnancy-Related and Neonatal Disorders
Keywords: Maternal and Neonatal Health
Early Disease Prediction
AI-Assisted Diagnosis
Lead sponsor: The Eye Hospital of Wenzhou Medical University
Sponsor class: OTHER
First posted: 2025-01-24
Enrollment: 1000000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 45
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: OTHER
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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Area Under the Curve (AUC)
timeFrame: 1 year
measure: F1 Score
timeFrame: 1 year
Secondary outcomes:
measure: Sensitivity (True Positive Rate)
timeFrame: 1 year
measure: Specificity (True Negative Rate)
timeFrame: 1 year
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Guangzhou Women and Children's Medical Center
state: Guangdong
city: Wenzhou
country: China
facility: First Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
city: Wenzhou
country: China
facility: Second Affiliated Hospital of Wenzhou Medical University
state: Zhejiang
Study officials:
References: