Physics-Guided Machine Learning for Non-Invasive Intracranial Pressure Measurement
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Solution Overview
Problem
Conventional methods for measuring intracranial pressure (ICP) and intracranial elastance (ICE) are invasive, prone to complications, and non-invasive techniques lack accuracy, while existing non-invasive methods like transcranial Doppler ultrasound are uncertain and difficult to use.
Innovation Solution
A physics-guided machine learning model is used to non-invasively measure ICP and ICE by combining acoustic measurement data from ultrasound with other information, determining cerebral blood flow velocity and arterial blood pressure to generate inputs for the model, which then outputs ICP and ICE measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive methods are used to measure ICP and ICE, then measurement accuracy is improved, but patient safety and ease of operation deteriorate due to complications and invasiveness
Solution Approach 1:
The patent replaces invasive mechanical pressure sensors with a non-invasive acoustic measurement system. Ultrasound waves are used to detect brain tissue motion and blood flow, which are then processed by machine learning models to estimate ICP and ICE, eliminating the need for physical insertion into the brain while maintaining measurement capability
Solution Approach 2:
The patent introduces acoustic waves as an intermediary medium to indirectly measure ICP and ICE. Instead of directly sensing pressure, the system uses ultrasound to detect tissue displacement and blood flow velocity, which serve as intermediate indicators that correlate with intracranial pressure and elastance
2Object-affected harmful factors
If non-invasive acoustic methods are used to measure ICP, then patient safety is improved, but measurement accuracy and reliability deteriorate compared to invasive methods
Solution Approach 1:
The patent implements feedback loops where the machine learning models continuously refine their predictions by comparing acoustic measurement patterns with expected physiological responses. The system adjusts its estimation algorithms based on the relationship between measured brain tissue motion, blood flow velocity, and predicted pressure values, improving accuracy over time
Solution Approach 2:
The patent combines multiple measurement modalities into a composite assessment system. It integrates acoustic impedance data, Doppler blood flow velocity, and machine learning-derived estimates to create a composite measurement approach that compensates for the limitations of individual methods and improves overall measurement reliability
3Ease of operation
If conventional non-invasive methods like transcranial Doppler are used, then ease of operation is improved, but measurement reliability and accuracy deteriorate due to uncertainty
Solution Approach 1:
The patent creates a multi-functional measurement system that simultaneously assesses multiple physiological parameters including brain tissue motion, blood flow velocity, and acoustic impedance. This universal approach allows the same acoustic device to gather diverse data types that collectively improve measurement reliability while maintaining non-invasive simplicity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides accurate and reliable non-invasive measurements of ICP and ICE, reducing the risks associated with invasive methods and improving upon the limitations of current non-invasive techniques.
Implementation Method 1
obtain acoustic measurement data obtained from measuring acoustic signals from the subject's brain
Implementation Method 2
determining a cerebral blood flow velocity (CBFV) measurement of the subject's brain using the acoustic measurement data
Data Source
AI summary
Described herein are techniques for non-invasively measuring intracranial ICP in a subject's brain. Some embodiments use a physics guided machine learning model to determine measurements of various metrics (e.g., ICP, ABP, and/or ICE) of a subject's brain. The structure of the physics guided machine learning model may be based on a model of the brain (e.g., a hemodynamic or elastic model of the brain). The physics guided machine learning model may include various machine learning models (e.g., neural networks) representing different aspects of the brain's fluid dynamics and/or mechanics. The techniques may use acoustic measurement data (e.g., obtained using ultrasound) in conjunction with other information to generate inputs for the physics guided machine learning model. The inputs may be used to measurements of a metric for the subject's brain.