Blood T2 Risk Assessment for Hidden Cardiometabolic Screening
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Solution Overview
Problem
Conventional medical examinations fail to detect hidden cardiometabolic risk factors in asymptomatic individuals, leading to undiagnosed conditions such as prediabetes and atherosclerosis, necessitating more sensitive and accessible screening tools.
Innovation Solution
A method and system utilizing T2 relaxation time constants of blood combined with additional parameters, fed into algorithms for cardiometabolic health assessment, followed by communication of risk and implementation of treatment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional medical examinations are used, then the examination process is simple and accessible, but hidden cardiometabolic risk factors cannot be detected
Solution Approach 1:
The patent combines multiple parameters including T2 relaxation time constant, T1 relaxation time constant, and other clinical parameters into a single integrated assessment system. This merging of multiple measurement dimensions enables detection of hidden cardiometabolic risks that individual tests cannot detect alone, while maintaining a unified workflow that manages complexity.
Solution Approach 2:
The assessment system is designed to evaluate multiple cardiometabolic conditions simultaneously using the same set of parameters and algorithm. The system can detect various hidden risks including prediabetes, atherosclerosis, and other cardiometabolic disorders through a single comprehensive examination, making the tool universally applicable for different screening needs.
2Measurement precision
If T2 relaxation time constant combined with multiple parameters and algorithms is used, then detection sensitivity for hidden risks improves, but the assessment complexity increases
Solution Approach 1:
The patent replaces complex manual clinical assessment with an automated algorithm that processes multiple parameters including T2 relaxation time constant, T1 relaxation time constant, and other clinical data. This computational approach handles the complexity of multi-parameter integration while providing consistent, objective risk assessment without requiring manual interpretation of multiple test results.
Solution Approach 2:
The system utilizes changes in relaxation time constants (T2 and T1) as key parameters to detect subtle physiological changes associated with cardiometabolic risks. By monitoring these specific biophysical parameters that change with metabolic state, the system achieves high detection sensitivity for hidden conditions before they manifest as obvious clinical symptoms.
3Reliability
If early screening of asymptomatic individuals is implemented, then health outcomes improve, but the need for more sophisticated screening tools increases complexity
Solution Approach 1:
The patent enables preliminary detection of cardiometabolic risks in asymptomatic individuals before clinical symptoms develop. By using T2 relaxation time constant and other parameters to identify hidden risks early in the disease process, the system allows for preliminary intervention and prevention strategies to be implemented before conditions progress to overt disease states, improving overall health outcomes.
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
Detects hidden cardiometabolic risks, enabling early intervention and management of conditions like diabetes and cardiovascular disease, improving health outcomes in asymptomatic individuals.
Implementation Method 1
a T2 relaxation time constant of a subject's blood (T2 value)
Data Source
AI summary
Embodiments pertain to a method of assessing the cardiometabolic health of a subject by (1) receiving a plurality of parameters of the subject, where the plurality of parameters includes a T2 relaxation time constant of a subject's blood (T2 value) and one or more additional parameters; and (2) feeding the plurality of parameters into an algorithm, where the algorithm correlates the plurality of parameters to the subject's cardiometabolic health. The methods may also include a step of communicating cardiometabolic health risk. The methods may also include a step of implementing a treatment decision. Further embodiments pertain to system for assessing the cardiometabolic health of a subject in accordance with such methods.