Health TrialsbyHealthPanel

Early Diagnosis and Prediction of Maternal and Neonatal Diseases:

ConditionPregnancy-Related and Neonatal Disorders
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-45
SponsorThe Eye Hospital of Wenzhou Medical University

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

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.

Disqualifiers

Women under 18 or over 45 years old.

Participants with insufficient follow-up data or missing critical clinical information required for predictive modeling.

Trial 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.

Trial design

Design model

Case-control

Time perspective

Other

Treatments tested in this trial

  • 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.

Treatment groups

1,000,000 Participants
are divided into 2 treatment groups
Group A: Healthy Maternal and Neonatal Cohort1 intervention
Group B: Maternal and Neonatal Disease Cohort1 intervention

Trial outcomes

Primary outcomes

1

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).

Time frame
1 year
2

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.

Time frame
1 year

Secondary outcomes

1

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.

Time frame
1 year
2

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.

Time frame
1 year

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

The Eye Hospital of Wenzhou Medical University

Lead sponsor

Wenzhou Medical University

Sponsor institution

Source ClinicalTrials.gov
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