Deep Learning Model for Myocardial Contraction Analysis
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Solution Overview
Problem
Current methods for modeling myocardial mechanics require substantial computational resources and time, making them unsuitable for clinical applications, especially when limited clinical data is available.
Innovation Solution
A deep learning model is developed to assess and predict the behavior of an active contraction model of the left ventricular myocardium using limited clinical parameters and pressure-volume loops, allowing for patient-specific analyses without extensive data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational methods are used to model myocardial mechanics, then modeling accuracy is improved, but computational time and resource requirements increase substantially
Solution Approach 1:
The patent creates a simplified copy of the complex myocardial mechanics model by training a deep learning model on data generated from traditional computational methods. The trained DL model then serves as a lightweight replica that can predict myocardial mechanics outcomes without requiring the substantial computational resources of the original model, thus resolving the contradiction between accuracy and computational time.
Solution Approach 2:
The patent performs preliminary computational work by generating training data using traditional accurate methods, then uses this pre-processed data to train the deep learning model. Once trained, the model can make rapid predictions without repeating the computationally intensive processes, effectively moving the computational burden to an initial offline phase rather than real-time application.
2Measurement precision
If comprehensive clinical data is collected for myocardial analysis, then model accuracy is improved, but data processing complexity and time increase
Solution Approach 1:
The patent extracts and focuses only on the most critical clinical parameters needed for myocardial mechanics analysis, rather than processing comprehensive clinical datasets. By identifying and isolating the essential features (pressure-volume loop data and key clinical metrics), the model achieves accurate predictions while avoiding the complexity of processing unnecessary data.
Solution Approach 2:
The patent uses a limited subset of clinical data (partial action) rather than comprehensive datasets. The deep learning model is designed to work effectively with minimal input parameters, demonstrating that full data collection is not necessary when the right features are identified, thus reducing processing complexity while maintaining accuracy.
3Reliability
If detailed fiber orientation data is obtained, then tissue behavior analysis is improved, but measurement and processing difficulty increase
Solution Approach 1:
The patent introduces an intermediary approach by using deep learning to infer detailed fiber orientation patterns from easily measurable clinical parameters. Rather than directly measuring complex fiber orientations, the model learns the relationship between simple clinical data and fiber orientation patterns, acting as an intermediary that translates accessible measurements into detailed tissue characterization.
Data Source
AI summary
A deep learning model can be used for the identification of active contraction properties of the myocardium using limited clinical methods. A method for identifying the active contraction properties can include inputting a plurality of clinical metrics into a deep learning model. The method can further include inputting a representation of a cardiac cycle through a pressure volume-loop into the deep learning model. The deep learning model can include a first process layer with a first intermediate output and a second process layer that receives the first intermediate output as a first intermediate input. The method can further include outputting one or more contraction properties of the myocardium.