Integrated Multi-Scale and Molecular Analysis for Disease Management
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Solution Overview
Problem
Current clinical management of diseases like cardiovascular disease is challenging due to the lack of integrated analysis of molecular, imaging, and clinical data, leading to sub-optimal diagnosis and treatment, as each type of data is analyzed independently without a systematic and inter-dependent approach.
Innovation Solution
A system that integrates molecular, imaging, and clinical data using a multi-scale model, a molecular model, and a linking component to generate estimated multi-scale parameters and molecular findings, enabling a comprehensive analysis of disease progression and therapy planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If molecular evaluation is performed to identify biomarkers of disrupted molecular pathways, then early detection of disease onset is improved, but information regarding the location or dimension of the lesion is lost
Solution Approach 1:
The patent combines molecular evaluation data with imaging data in an integrated analysis system. The molecular model identifies disrupted pathways and biomarkers, while the multi-scale model processes imaging data to provide spatial and dimensional information. These models are linked to merge their outputs, ensuring that early detection from molecular data is complemented by location and dimension information from imaging data, thus resolving the information loss.
2Loss of information
If imaging is used to quantify spatial and dynamic changes in heart morphology, then location and dimension of lesions are identified, but early stages of disease without visible symptoms are not detected
Solution Approach 1:
The integrated analysis system merges imaging data that provides spatial and dimensional information with molecular evaluation data that detects early biochemical changes. The molecular model identifies disrupted pathways before structural changes occur, while the multi-scale model integrates this with imaging data to provide both early detection and precise location/dimension information simultaneously.
3Adaptability or versatility
If data analysis is performed independently by different persons, then specialized expertise is utilized, but sub-optimal diagnosis and contradictory conclusions result
Solution Approach 1:
The patent merges independently analyzed data types into a unified integrated analysis system. The molecular model, multi-scale model, and linking component work together in a coordinated framework that combines specialized analyses while ensuring consistency through systematic integration. This produces a unified diagnostic conclusion that incorporates all specialized expertise without contradictory results.
Solution Approach 2:
The system implements feedback mechanisms where the integrated analysis results are used to refine and coordinate the different data analysis components. The linking component facilitates bidirectional information flow between models, allowing each specialized analysis to inform and adjust the others, ensuring consistent and optimized diagnostic conclusions.
4Reliability
If a systematic and inter-dependent analysis framework is implemented, then diagnostic accuracy and treatment optimization are improved, but system complexity increases
Solution Approach 1:
The patent segments the complex integrated analysis system into distinct functional modules: a molecular model for pathway analysis, a multi-scale model for imaging and clinical data, and a linking component for integration. This segmentation manages complexity by organizing the systematic analysis into manageable, specialized components that can be developed and maintained independently while working together cohesively.
Data Source
AI summary
A system operating in a plurality of modes to provide an integrated analysis of molecular data, imaging data, and clinical data associated with a patient includes a multi-scale model, a molecular model, and a linking component. The multi-scale model is configured to generate one or more estimated multi-scale parameters based on the clinical data and the imaging data when the system operates in a first mode, and generate a model of organ functionality based on one or more inferred multi-scale parameters when the system operates in a second mode. The molecular model is configured to generate one or more first molecular findings based on a molecular network analysis of the molecular data, wherein the molecular model is constrained by the estimated parameters when the system operates in the first mode. The linking component, which is operably coupled to the multi-scale model and the molecular model, is configured to transfer the estimated multi-scale parameters from the multi-scale model to the molecular model when the system operates in the first mode, and generate, using a machine learning process, the inferred multi-scale parameters based on the molecular findings when the system operates in the second mode.