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.
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
1,000,000
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.
Guangzhou Women and Children's Medical Center
Guangzhou, Guangdong, China
RECRUITINGFirst Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China
RECRUITINGSecond Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang, China
RECRUITINGArea 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
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
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
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
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