Spatiotemporal Analysis of Echocardiography Videos for MACE Prediction
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Solution Overview
Problem
Current computer vision tools for analyzing medical images, particularly echocardiography data, face challenges due to noise and poor resolution, limiting their effectiveness in predicting major adverse cardiovascular events (MACE) in patients with chronic kidney disease (CKD).
Innovation Solution
A computer vision assessment system that performs a spatiotemporal analysis of anatomic videos, combining hand-crafted features such as radiomic features with deep learning models to generate a biomarker for predicting MACE. This system processes echocardiography videos frame by frame, extracting spatial and temporal features to improve predictive accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer vision tools are used to analyze echocardiography data, then the analysis process is simple, but the predictive accuracy is limited due to noise and poor resolution
Solution Approach 1:
The patent combines multiple feature extraction approaches (hand-crafted radiomic features and deep learning features) into a unified predictive model. This integration allows the system to leverage both traditional image analysis methods and modern neural network capabilities, thereby improving predictive accuracy for MACE while systematically managing the complexity through structured feature fusion
Solution Approach 2:
The analysis system is segmented into distinct functional components: radiomic feature extraction module, deep learning feature extraction module, and predictive modeling module. This segmentation allows each component to specialize in specific tasks, improving overall predictive accuracy while making the complex system more manageable and interpretable
2Measurement precision
If hand-crafted radiomic features are used alone, then the system is interpretable, but the predictive performance is insufficient for MACE risk assessment
Solution Approach 1:
The patent creates a composite feature representation by combining radiomic features (which provide interpretability) with deep learning features (which capture complex patterns). This composite approach ensures that no critical information is lost, as each feature type complements the other's strengths and compensates for its weaknesses in predicting MACE outcomes
3Measurement precision
If deep learning models are used alone, then the predictive accuracy improves, but the system becomes less interpretable and more complex
Solution Approach 1:
The patent introduces radiomic features as an intermediary that bridges the gap between deep learning models and clinical interpretability. These hand-crafted features serve as a common language that connects the complex deep learning representations with clinically meaningful parameters, maintaining ease of operation and interpretability while leveraging the predictive power of deep learning
4Measurement precision
If static image analysis is used, then the processing is fast, but the temporal dynamics of cardiac morphology are not captured
Solution Approach 1:
The patent analyzes echocardiography videos at multiple temporal phases (systole and diastole) to capture the periodic cardiac cycle dynamics. By extracting features at these critical periodic moments, the system captures essential temporal information about cardiac morphology changes without requiring continuous analysis of every frame, thus maintaining reasonable processing speed while improving prognostic accuracy
Data Source
AI summary
The present disclosure relates to a method. The method includes operating a first spatiotemporal model upon a plurality of frames of an anatomic video of a patient to determine a first prediction. The first spatiotemporal model is configured to determine the first prediction using a plurality of hand-crafted features extracted from the plurality of frames. A second spatiotemporal model having one or more deep learning models is operated upon the plurality of frames of the anatomic video to determine a second prediction. A medical prediction is generated based upon a combination of the first prediction and the second prediction.