Pregnancy-Related and Neonatal DisordersEDPMNDNCT06791343
Early Diagnosis and Prediction of Maternal and Neonatal Diseases:
An observational study of Pregnancy-Related and Neonatal Disorders, run by The Eye Hospital of Wenzhou Medical University at 3 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 45 yearsHealthy volunteers can take part
- Conditions the study is for
- Pregnancy-Related and Neonatal Disorders
- What is being tested?
- AI-Based Diagnostic and Prognostic Model
- How many people
- 1,000,000 participants
- Where
- 3 sitesChina
- Study dates
- 1 Aug 2023to May 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 45 years
- All sexes
- Healthy volunteers accepted
- Adult
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.
Who may be able to join
- 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.
Who cannot join
- Women under 18 or over 45 years old.
- Participants with insufficient follow-up data or missing critical clinical information required for predictive modeling.
Trial locations and contacts
3 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China3 sites, 3 recruiting
- Recruiting
Guangzhou Women and Children's Medical Center
Guangzhou, Guangdong
Bingzhou Liu, MD, +86-0756-2222569, mr_jerry_99@163.com
- Recruiting
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Cheng Tang, MD, +86-0577-55579999, c249325687@163.com
- Recruiting
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Sian Liu, MD, +86-0577-88002888, liusan@mail3.sysu.edu.cn
About this study
Official title: 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.
Read the 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.
- Conditions
- Pregnancy-Related and Neonatal Disorders
- Being tested
- AI-Based Diagnostic and Prognostic Model
- Keywords
- Maternal and Neonatal Health, Early Disease Prediction, AI-Assisted Diagnosis
- Medical subject headings
- Infant, Newborn, Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Diagnostic and Prognostic ModelDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: AI-Based Diagnostic and Prognostic Model | |
| 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. Receives: Diagnostic Test: AI-Based Diagnostic and Prognostic Model |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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.
Measured: 1 year
Secondary outcomes (2)
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.
Measured: 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.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jan 2025
- Study start1 Aug 2023
- Primary completionMay 2025 (estimated)
- Study completionMay 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pregnancy-Related and Neonatal DisordersStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Other
- Enrolment
- 1,000,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Eye Hospital of Wenzhou Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kang Zhang, Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06791343
- Sponsor's protocol ID
- Maternal and Neonatal Diseases
- Acronym
- EDPMND
- Status last verified
- Apr 2025
- First submitted
- 19 Jan 2025
- Last updated
- 17 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early Prediction and Diagnosis of Maternal and Neonatal Diseases Using Multimodal Health Data
- ClinicalTrials.gov ID
- NCT06791343
- Acronym / sponsor ID
- EDPMND / Maternal and Neonatal Diseases
- Conditions
- Pregnancy-Related and Neonatal Disorders
- Keywords
- Maternal and Neonatal Health, Early Disease Prediction, AI-Assisted Diagnosis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000,000 participants (estimated)
- Lead sponsor
- The Eye Hospital of Wenzhou Medical UniversityUniversity, hospital or other
- Study start
- 1 Aug 2023 (actual)
- Primary completion
- May 2025 (estimated)
- Study completion
- May 2025 (estimated)
- First posted
- 24 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case-control
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Healthy Maternal and Neonatal Cohort | – | Diagnostic Test: AI-Based Diagnostic and Prognostic ModelThis 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. |
| Maternal and Neonatal Disease Cohort | – | Diagnostic Test: AI-Based Diagnostic and Prognostic ModelThis 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. |
Who may qualify
- Ages
- 18 to 45 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (2 listed)
- Women under 18 or over 45 years old.
- Participants with insufficient follow-up data or missing critical clinical information required for predictive modeling.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Based Diagnostic and Prognostic ModelThis 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. | Diagnostic test | Healthy Maternal and Neonatal Cohort, Maternal and Neonatal Disease Cohort |
Primary outcome
Area Under the Curve (AUC)
Time frame: 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
Time frame: 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.
2 secondary outcomes
Sensitivity (True Positive Rate)
Time frame: 1 year
Specificity (True Negative Rate)
Time frame: 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Apr 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pregnancy-Related and Neonatal Disorders, run by The Eye Hospital of Wenzhou Medical University at 3 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 17 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Fei Liu, MD (+86 13810512704, liufei_2359@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Curve (AUC) (time frame: 1 year). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Apr 2025.