Early Diagnosis and Prediction of Maternal and Neonatal Diseases:

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.

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.

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.