Multiplex Biomarker Algorithm for Shock Diagnosis
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Solution Overview
Problem
Current medical diagnostics for shock are limited by the inaccuracy of non-invasive measurements, which often rely on single-point assessments of tissue and organ parameters, failing to provide adequate early detection and diagnosis, especially in hemodynamic instability situations.
Innovation Solution
A system utilizing multiple sensors placed at physiologically distinct locations to collect optical, electromagnetic, and temporal data, combined through machine learning algorithms to create a synthetic biomarker that outperforms traditional single-measurement methods, incorporating anatomic and temporal patterns for improved diagnostic performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If non-invasive single-point measurements are used, then device complexity is reduced, but measurement precision and diagnostic accuracy deteriorate
Solution Approach 1:
The measurement system is segmented into multiple independent sensor units positioned at different anatomical locations. Each sensor measures local tissue parameters independently, and the combined data provides comprehensive diagnostic information. This segmentation allows high precision measurements without requiring a single complex measurement system.
Solution Approach 2:
The approach transitions from single-point (0D) or single-location (1D) measurements to multi-location (2D/3D) spatial distribution of measurements. By adding the dimension of spatial distribution across multiple anatomical sites, the system achieves both high precision and comprehensive diagnostic capability without excessive complexity.
2Measurement precision
If multiple sensors at distinct locations are used, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The sensor system is designed with universal functionality where each sensor can measure multiple tissue parameters (oxygenation, hemodynamics, metabolism) and the same sensor type can be applied at different anatomical locations. This multi-functionality reduces overall system complexity while maintaining high diagnostic accuracy through standardized measurement protocols.
Solution Approach 2:
Multiple sensor measurements from different locations are merged and integrated through algorithms that combine the data into comprehensive diagnostic assessments. The merging process synthesizes information from distributed sensors to achieve diagnostic accuracy equivalent to or better than single-point measurements, while the modular sensor design keeps individual components simple.
3Ease of operation
If traditional single-measurement methods are used, then ease of operation is maintained, but reliability of diagnosis deteriorates
Solution Approach 1:
The system incorporates continuous feedback mechanisms where measurements from multiple locations are continuously monitored and compared. The feedback algorithms dynamically adjust diagnostic assessments based on the pattern of measurements across different anatomical sites, improving reliability while maintaining automated operation that is easy to interpret for clinicians.
Solution Approach 2:
The measurement system performs self-validation by comparing measurements across multiple locations and over time. The system automatically identifies consistent patterns that indicate true physiological states versus artifacts, providing reliable diagnostics without requiring complex manual operation or interpretation by the user.
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 enhances the accuracy of shock diagnosis and prediction by leveraging multiplex sensing and machine learning, providing a more comprehensive understanding of tissue and organ status, potentially guiding therapy and improving treatment outcomes.
Implementation Method 1
The transmission, absorption, and/or reflectance of near-infrared and infrared wavelengths into tissues for the measurement of various molecular species and the state of cells and tissues, such as the use of near-infrared spectroscopy to measure oxygen saturation of hemoglobin
Implementation Method 2
The body itself creates measurable electromagnetic fields, which are measured clinically by devices such as the electrocardiogram and electroencephalogram
Implementation Method 3
Using electromagnetic potential or impedance it is also possible to measure, or more likely approximate, a number of clinically important measures of hemodynamics. Among these are cardiac output and ventricular stroke
Data Source
AI summary
A method for diagnosing or predicting the risk of shock, the method incorporating an algorithmic combination of optical, electromagnetic, and other sensors, along with their anatomic and temporal patterns. A method for developing the algorithms through iterative optimization using machine learning.