Cardiac ML Panel for Diastolic Dysfunction Probability Tracking
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Solution Overview
Problem
Existing detection models for medically relevant features in biomedical data, such as cardiac dysfunction, are limited in accuracy and hinder efficient diagnostic decision-making due to the complexity of time series data analysis.
Innovation Solution
An apparatus and method using a processor and memory system that includes cardiac machine learning models to analyze cardiac input data, generating cardiac indices and displaying them through a graphical user interface, allowing for efficient tracking and prediction of cardiac conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cardiac models are used to analyze cardiac input data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the analysis by dividing cardiac data into multiple specialized models, each targeting specific cardiac conditions (e.g., systolic dysfunction, diastolic dysfunction, pulmonary hypertension). This segmentation allows each model to focus on detecting specific medically relevant features, thereby improving overall detection accuracy while maintaining manageable complexity through modular design.
Solution Approach 2:
The cardiac panel is designed as a universal multi-functional system that processes various types of cardiac input data (ECG, echocardiogram, MRI, CT) through multiple models. Each model can be independently configured for different cardiac conditions, allowing the same infrastructure to serve multiple diagnostic purposes simultaneously, improving precision without proportionally increasing complexity.
2Reliability
If multiple cardiac models are used to analyze cardiac input data, then reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system merges the outputs of multiple cardiac models into a unified cardiac panel interface. Instead of requiring users to separately analyze results from multiple independent models, the system combines their findings into a single integrated display, maintaining high diagnostic reliability while significantly improving ease of operation by presenting consolidated results in one location.
Solution Approach 2:
The graphical user interface acts as an intermediary between the complex multi-model system and the end user. It translates and presents the outputs from multiple specialized models in a unified, easy-to-interpret format, allowing users to benefit from the combined reliability of multiple models without needing to understand or manually synthesize their individual results.
3Loss of information
If time series data is analyzed using multiple models, then information completeness is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-configuring multiple specialized models to run simultaneously on cardiac input data. Rather than sequentially analyzing data through different models, the system prepares and executes multiple models in parallel, ensuring comprehensive information extraction while minimizing analysis time through efficient concurrent processing.
Data Source
AI summary
Apparatus for tracking cardiac indices and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to receive cardiac input data from a patient, input the cardiac input data into a cardiac panel including a plurality of cardiac models, at least one cardiac model of which is configured to calculate a cardiac index, includes at least one cardiac machine learning model, and is configured to calculate a cardiac index associated with diastolic dysfunction, generate one or more cardiac indices from the cardiac panel as a function of the cardiac input data and the cardiac machine learning model, wherein at least one cardiac index includes a probability of the patient satisfying at least one grading threshold, and display the at least one cardiac index through a graphical user interface.