Early Diagnosis and Prediction of Maternal and Neonatal Diseases:

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

1000000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-08-01

Study Completion Date

2025-05-31

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Pregnancy-Related and Neonatal Disorders

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

OTHER

Study Groups

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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.

AI-Based Diagnostic and Prognostic Model

Intervention Type 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.

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.

AI-Based Diagnostic and Prognostic Model

Intervention Type 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.

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

45 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes