Heart Failure Prognosis from Echocardiographic Video Features
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Solution Overview
Problem
Existing prognostic prediction models for heart failure rely heavily on static clinical indicators and biochemical markers, failing to comprehensively quantify the dynamic characteristics of cardiac function, limiting their accuracy in predicting patient prognosis.
Innovation Solution
A deep learning-based artificial neural network model processes echocardiographic video images to extract visual and temporal features, predicting prognosis without relying on expert interpretation, using a 3D encoder, transformer, and spatial attention pooling to generate integrated time series features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prognostic prediction models use standardized clinical variables and expert-defined parameters, then the model structure is simple and easy to implement, but the prediction accuracy is limited due to inability to comprehensively quantify dynamic cardiac characteristics
Solution Approach 1:
The patent replaces traditional mechanical/statistical prediction methods with a deep learning-based artificial neural network system. The model automatically extracts visual and temporal features from echocardiographic video images, substituting expert interpretation and standardized parameter analysis with automated image processing and feature extraction algorithms, thereby achieving comprehensive quantification of dynamic cardiac characteristics while improving prediction accuracy
Solution Approach 2:
The patent transforms static clinical indicators into dynamic visual and temporal features by processing echocardiographic video sequences. The model extracts time-varying characteristics from video frames, converting traditional static parameters into dynamic feature representations that capture the temporal evolution of cardiac function, thereby enabling more accurate prognosis prediction
2Reliability
If expert interpretation-based indicators are used in prediction models, then the model is easier to interpret clinically, but the prediction performance is limited due to human opinion involvement
Solution Approach 1:
The patent implements self-service by enabling the prediction model to automatically extract and process features without requiring expert interpretation. The deep learning system performs autonomous feature extraction from echocardiographic videos, generating prognostic predictions independently of human expert analysis, thereby eliminating the limitations of human opinion involvement while maintaining clinical interpretability through automated decision support
3Loss of information
If only static clinical indicators are used for prognosis prediction, then the data collection is simple and quick, but the dynamic characteristics of cardiac function cannot be comprehensively quantified
Solution Approach 1:
The patent applies preliminary action by pre-processing and extracting visual and temporal features from echocardiographic video images before prognosis prediction. The model performs automated feature extraction from video sequences in advance, converting raw video data into meaningful temporal features that capture dynamic cardiac characteristics, thereby preserving complete information while reducing the time required for clinical analysis
Data Source
AI summary
The present disclosure provides a method for providing prognostic information on heart failure implemented by a processor and a device for providing prognostic information on heart failure using the same, and the method includes receiving an echocardiographic video image of an individual suffering from heart failure and determining data on the prognosis of heart failure based on the received echocardiographic video image, using a prediction model trained to output data on the prognosis of the heart failure by taking the echocardiographic video image as a single input without any other input.