Multistage AI Model Integrating CTG and Maternal Data for Labor Predictions
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Solution Overview
Problem
Current AI-based systems for fetal and maternal health monitoring during labor are inadequate as they often rely on limited data inputs, are rule-based, and lack integration, which can impede clinicians' decision-making processes by not providing a comprehensive view of clinical parameters.
Innovation Solution
A multistage AI model that combines CTG analysis data and maternal health data to generate labor and delivery predictions, using machine learning techniques to analyze cardiotocography and maternal health parameters, providing a comprehensive view for clinical decision support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If discrete algorithms and systems are used to analyze clinical data, then the system complexity is reduced, but the measurement precision and comprehensiveness of clinical predictions deteriorate
Solution Approach 1:
The patent combines multiple discrete algorithms and systems into a unified AI-based platform that integrates CTG analysis, maternal health monitoring, and labor progression prediction. This merging allows comprehensive analysis of multiple clinical parameters simultaneously, improving prediction accuracy while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The AI-based system performs multiple functions including fetal heart rate analysis, maternal vital signs monitoring, labor progression prediction, and delivery outcome forecasting. This multi-functionality enables comprehensive clinical assessment within a single system, enhancing measurement precision without requiring multiple separate systems.
2Ease of operation
If rule-based algorithms are used for clinical predictions, then the ease of operation is improved, but the adaptability to complex clinical scenarios deteriorates
Solution Approach 1:
The system uses AI models that can dynamically adjust prediction parameters based on incoming clinical data patterns. The algorithms adapt to different clinical scenarios by learning from training data and adjusting their decision boundaries, allowing versatility while maintaining ease of operation through automated parameter optimization.
Solution Approach 2:
The AI-based system incorporates feedback loops where prediction outcomes are continuously refined based on actual clinical outcomes. This feedback mechanism enables the system to adapt to complex scenarios by learning from past performance, improving versatility while maintaining user-friendly operation through automated learning.
3Device complexity
If limited data inputs are used in AI models, then the device complexity is reduced, but the reliability of clinical predictions deteriorates
Solution Approach 1:
The system segments data processing into distinct modules: CTG data analysis, maternal health parameter monitoring, labor progression tracking, and delivery outcome prediction. Each module processes specific data types independently, reducing overall complexity while enabling comprehensive data integration that improves prediction reliability through multiple independent validation pathways.
Data Source
AI summary
One or more systems, devices, computer-implemented methods and/or computer program products of use provided herein relate to artificial intelligence (AI) to generate labor and delivery-based predictions. A system can comprise a processor that can execute computer-executable components stored in memory, wherein the computer-executable components can comprise a first AI model that can generate first data comprising one or more labor and delivery predictions applicable to one or more fetuses and a mother of the one or more fetuses, during labor, by analyzing second data comprising cardiotocography (CTG) analysis data of the one or more fetuses and the mother generated by a second AI model and third data comprising maternal health analysis data of the mother generated by a third AI model, wherein the first AI model can be a multistage AI model comprising respective models directed to predicting respective ones of the one or more labor and delivery predictions.