Hierarchical Analytics Framework for Non-Invasive Plaque Fissure Detection
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Solution Overview
Problem
Current medical imaging techniques face challenges in effectively utilizing high spatial and temporal resolution data, leading to information overload for clinicians, and lack accurate methods for non-invasive assessment of cardiovascular disease risk, particularly in identifying high-risk plaques and fractional flow reserve, which results in misclassification and overtreatment of patients.
Innovation Solution
A hierarchical analytics framework that combines computerized image analysis and data fusion algorithms with clinical chemistry and blood biomarker data to provide a multi-factorial panel for distinguishing between disease subtypes, using convolutional neural networks for phenotyping and risk stratification, and incorporating plaque morphology for accurate assessment of vascular health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high spatial and temporal resolution imaging data is collected, then diagnostic information quality is improved, but information overload for clinicians occurs
Solution Approach 1:
The patent extracts and isolates specific high-risk plaque features (positive remodeling, low attenuation, spotty calcification, plaque rupture) from the complete imaging dataset. By focusing only on these critical morphological characteristics rather than processing all imaging data, the system maintains diagnostic precision while preventing information overload for clinicians.
Solution Approach 2:
The patent segments the complex imaging data into distinct plaque morphology categories and risk stratification levels. By dividing the continuous imaging data into discrete, clinically relevant classifications (high-risk vs. low-risk plaque), the system preserves diagnostic information quality while making the data manageable and interpretable for clinical decision-making.
2Ease of operation
If traditional imaging methods are used for plaque assessment, then procedural simplicity is maintained, but misclassification and overtreatment of patients occurs
Solution Approach 1:
The patent replaces subjective visual assessment by clinicians with automated computerized image analysis algorithms. The system uses computational methods to objectively quantify plaque morphology features (positive remodeling index, attenuation values, calcification patterns), thereby maintaining procedural simplicity while dramatically improving classification reliability and reducing misdiagnosis.
Solution Approach 2:
The patent introduces an intermediary automated analysis system between the imaging modality and clinical decision-making. This intermediary layer processes imaging data through standardized algorithms that apply established criteria for high-risk plaque identification, ensuring consistent and reliable patient classification without adding complexity to the clinical workflow.
3Reliability
If invasive procedures are performed for accurate cardiovascular risk assessment, then diagnostic reliability is improved, but patient risk and healthcare costs increase
Solution Approach 1:
The patent creates a virtual copy of invasive assessment capabilities through non-invasive imaging combined with automated morphological analysis. By using coronary CT angiography with sophisticated plaque characterization algorithms, the system replicates the diagnostic reliability previously achievable only through invasive angiography and histology, thereby eliminating patient risk and reducing healthcare costs associated with invasive procedures.
Solution Approach 2:
The patent changes the assessment parameters from invasive physiological measurements to non-invasive morphological and compositional parameters. By quantifying plaque features such as positive remodeling, low attenuation areas, and spotty calcification patterns visible on CT imaging, the system achieves reliable cardiovascular risk assessment without the harms of invasive procedures while maintaining diagnostic accuracy.
Data Source
AI summary
Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.