Neural Network Predicting Sudden Cardiac Death Survival
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Solution Overview
Problem
Current methods for predicting sudden cardiac death (SCD) are limited by their inability to provide personalized, accurate, and cost-effective arrhythmia risk assessments, often failing to account for patient-specific clinical features and time-evolution of the disease.
Innovation Solution
A computer-implemented neural network method that combines cardiac image data and covariate data to predict patient-specific arrhythmic sudden cardiac death survival probabilities, using a subnetwork architecture that includes an encoder-decoder convolutional subnetwork for image data and a dense subnetwork for covariate data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning the features of scar distribution is used for risk analysis, then prediction accuracy is improved, but computational complexity increases making it impractical as a first stage screening tool
Solution Approach 1:
The system segments the population into risk subgroups based on scar distribution features, then applies different levels of analysis to each subgroup. This allows comprehensive risk assessment for high-risk patients while maintaining efficiency for low-risk patients, resolving the contradiction between accuracy and computational complexity.
Solution Approach 2:
The patent introduces an intermediary screening approach that uses simplified risk assessment metrics to identify high-risk patients who then receive the more computationally intensive scar distribution analysis. This intermediary layer prevents unnecessary complex computations for low-risk patients while ensuring accurate assessment for those who need it.
2Productivity
If broad population stratification based on subgroups is used, then computational efficiency is maintained, but personalized predictions for individual patient features are lost
Solution Approach 1:
The system applies local quality by providing different levels of prediction detail to different patients based on their risk profile. High-risk patients receive personalized predictions incorporating their specific scar distribution features, while low-risk patients receive efficient subgroup-based assessments, optimizing both computational efficiency and personalization.
Solution Approach 2:
The prediction system is dynamic, adapting the level of analysis performed for each patient based on their initial risk assessment. This allows the system to maintain computational efficiency for the majority of patients while providing personalized predictions when clinically necessary, resolving the contradiction between efficiency and personalization.
3Ease of operation
If pre-defined finite time points are used for SCDA risk assessment, then assessment simplicity is maintained, but patient-specific time-evolution of the disease is ignored
Solution Approach 1:
The system performs periodic risk assessments at clinically relevant time points while also providing continuous risk estimation methodology. This allows maintaining the simplicity of scheduled assessments while capturing the time-evolution of disease risk through the underlying continuous model, resolving the contradiction between simplicity and information retention.
Data Source
AI summary
Computer-implemented neural network techniques for predicting patient-specific arrhythmic sudden cardiac death survival are presented. The techniques can include obtaining cardiac image data for a patient; obtaining cardiac covariate data for the patient; providing the cardiac image data to a first subnetwork; providing the cardiac covariate data to a second subnetwork; combining an output from the first subnetwork with an output from the second subnetwork to produce survival probability data; and outputting patient-specific arrhythmic sudden cardiac death survival prediction data based on the survival probability data.