Temporal Alignment Models for Gestational Age and Delivery Timing
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Solution Overview
Problem
Current methods for estimating gestational age and predicting time to delivery, such as ultrasound, are inaccurate and imprecise, particularly after the first trimester, leading to suboptimal clinical interventions and high rates of preterm births and neonatal morbidity.
Innovation Solution
A computational model trained on temporally aligned analyte measurements from pregnant individuals, including metabolites, proteins, and genomic data, to determine gestational age and time to delivery, replacing or complementing traditional methods like ultrasound.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ultrasound is used for estimating gestational age, then the method is non-invasive and widely available, but the accuracy is suboptimal with only 40% of newborns delivered within 7 days of the predicted due dates
Solution Approach 1:
The patent replaces the mechanical imaging system (ultrasound) with a biochemical analytical system that measures metabolites, proteins, and other analytes in blood or urine samples. This substitution enables more precise gestational age estimation through temporal alignment of multiple analyte measurements, achieving accuracy that surpasses traditional ultrasound methods.
Solution Approach 2:
The patent changes the measurement parameters from structural imaging (ultrasound) to biochemical parameters (concentrations of metabolites, proteins, and other analytes). By measuring multiple analytes at different time points and temporally aligning these measurements, the system achieves superior precision in gestational age estimation and time to delivery prediction.
2Measurement precision
If traditional ultrasound methods are used, then the equipment and procedure are simple, but the accuracy decreases after the first trimester
Solution Approach 1:
The patent replaces the relatively simple ultrasound equipment with a more complex biochemical analysis system that requires multiple analyte measurements at different time points. However, this increased complexity in measurement capability enables significantly improved accuracy throughout the entire pregnancy duration, particularly in the second and third trimesters where ultrasound accuracy deteriorates.
Solution Approach 2:
The patent implements continuous monitoring of multiple analytes throughout pregnancy at different time points, creating a continuous profile of gestational progress. This continuous measurement approach maintains high accuracy across all trimesters by repeatedly sampling biochemical markers that change systematically during pregnancy, rather than relying on single-time-point ultrasound imaging.
3Measurement precision
If multiple analyte measurements are taken at different time points, then the accuracy of gestational age estimation improves, but the complexity of data collection and temporal alignment increases
Solution Approach 1:
The patent employs temporal alignment algorithms that continuously refine gestational age estimates by comparing analyte measurements at different time points. The system uses feedback from multiple measurements to adjust and improve the gestational age calculation, automatically handling the complexity of temporal alignment through computational algorithms that process the time-series data systematically.
Solution Approach 2:
The patent introduces temporal alignment as an intermediary computational step that bridges the raw analyte measurements and the final gestational age estimation. This intermediary process automatically handles the complexity of synchronizing multiple time-point measurements, transforming complex multi-timepoint data into accurate gestational age predictions through algorithmic alignment.
Data Source
AI summary
Methods to compute gestational age and time to delivery utilizing temporal alignment methods and applications thereof are described. Generally, systems utilize analyte measurements collected at one or more time points to determine a gestational age and time to delivery, which can be used as a basis to perform interventions and treat individuals. Computational models trained utilizing temporal alignment of analyte measurements can be used to determine gestational age and time to delivery.