CTCS Imaging Feature Extraction for Validated Heart Failure Risk Prediction
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Solution Overview
Problem
Current models for heart failure risk prediction are not widely validated, hindering the pre-emptive initiation of therapies in at-risk patients.
Innovation Solution
A method and apparatus that utilize pathophysiological pathway related features extracted from digitized imaging data, such as CTCS images, to generate a medical prediction of heart failure risk using a machine learning stage, incorporating segmentation tools and feature extraction techniques to identify regions of interest and extract spatial and texture radiomic features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current models for heart failure risk prediction are used, then prediction capability is provided, but the models are not widely validated which hinders pre-emptive initiation of therapies
Solution Approach 1:
The patent performs comprehensive validation of the heart failure risk prediction model during the development phase using multiple independent cohorts and diverse imaging modalities. This preliminary validation ensures the model is ready for immediate clinical deployment without requiring additional validation studies, thus enabling pre-emptive therapy initiation.
2Measurement precision
If machine learning models with multiple features are used for heart failure prediction, then prediction accuracy is improved, but model complexity increases
Solution Approach 1:
The patent segments the prediction model into distinct modules: imaging feature extraction module, clinical data processing module, and risk prediction module. Each module processes specific types of data independently, then integrates results. This segmentation maintains high prediction accuracy through comprehensive feature analysis while reducing overall system complexity through modular design.
Solution Approach 2:
The patent develops a multi-functional prediction system that processes multiple imaging modalities (CT, MRI, echocardiography) and clinical data types through a unified machine learning framework. This universal approach improves prediction accuracy by leveraging diverse data sources while avoiding the complexity of developing separate models for each data type.
3Reliability
If comprehensive imaging data analysis is performed, then heart failure risk identification is improved, but screening cost and time increase
Solution Approach 1:
The patent performs preliminary analysis of imaging data to identify key radiomic features and patterns associated with heart failure risk before final prediction. This preliminary feature extraction and filtering reduces the dimensionality of the data, enabling comprehensive risk identification while significantly reducing processing time for clinical deployment.
Data Source
AI summary
The present disclosure, in some embodiments, relates to a method. The method includes accessing digitized imaging data stored in a memory. The digitized imaging data corresponds to a patient. A plurality of pathophysiological pathway related features are extracted from the digitized imaging data. The plurality of pathophysiological pathway related features correspond to one or more pathophysiological pathways relating to heart failure. The plurality of pathophysiological pathway related features are provided to a machine learning stage. The machine learning stage is configured to generate a medical prediction of heart failure risk for the patient using the plurality of pathophysiological pathway related features.