AI Screening of A4C Echocardiography for Rapid HFpEF Detection
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Solution Overview
Problem
Current diagnostic methods for heart failure with preserved ejection fraction (HFpEF) rely heavily on clinician expertise and are often delayed due to the complexity of multi-angle echocardiography and numerous cardiac function parameters, necessitating an efficient AI-assisted diagnostic pre-screening tool.
Innovation Solution
A method utilizing apical 4-chamber (A4C) view images for HFpEF diagnosis, involving image segmentation with U-net, feature extraction via convex hull algorithm, and training with one-dimensional convolutional neural networks (CNN) to identify HFpEF and administer appropriate medicaments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional multi-angle echocardiography and numerous cardiac function parameters are used for HFpEF diagnosis, then diagnostic accuracy is improved, but examination time and operational complexity increase
Solution Approach 1:
The patent extracts and focuses on a single critical view (apical 4-chamber view) from the comprehensive echocardiography protocol, isolating the most diagnostically valuable information while discarding redundant multi-angle examinations. This extraction approach maintains diagnostic accuracy for HFpEF while significantly reducing examination time and operational complexity.
Solution Approach 2:
The patent creates a simplified copy or representation of the comprehensive echocardiography diagnosis using only apical 4-chamber view images combined with AI deep learning analysis. This copying approach allows the system to capture essential diagnostic features without requiring the full complexity of multi-angle examinations, thereby reducing time loss while maintaining measurement precision.
2Reliability
If traditional clinician-based ultrasound assessment is used for HFpEF diagnosis, then diagnostic expertise is maintained, but treatment delay increases
Solution Approach 1:
The patent replaces the mechanical process of manual clinician assessment with an AI deep learning system that automatically analyzes apical 4-chamber view images. This substitution maintains diagnostic reliability by using trained neural networks that have learned from extensive medical data, while eliminating the time delays associated with manual interpretation and enabling rapid treatment initiation.
Solution Approach 2:
The AI deep learning system performs self-service diagnosis by automatically analyzing ultrasound images and generating diagnostic conclusions without requiring clinician intervention for each assessment. This self-service capability maintains the reliability of expert diagnosis through trained algorithms while dramatically reducing treatment delay through automated, immediate results.
3Measurement precision
If comprehensive multi-angle echocardiography is performed for HFpEF diagnosis, then diagnostic thoroughness is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The patent extracts and isolates the apical 4-chamber view as the single most critical echocardiographic image for HFpEF diagnosis, separating this essential view from the complex multi-angle examination protocol. This extraction maintains diagnostic thoroughness by focusing on the most informative perspective while significantly reducing device complexity and operational difficulty.
Solution Approach 2:
The patent segments the comprehensive echocardiography examination into discrete, manageable components, identifying and prioritizing the apical 4-chamber view as the essential segment for HFpEF diagnosis. This segmentation approach maintains diagnostic thoroughness through targeted analysis of the critical segment while reducing overall device complexity by eliminating unnecessary multi-angle components.
Data Source
AI summary
Disclosed herein is an AI-assisted screening method and model that capable of automatically extracting prominent intrabeat dynamic patterns associated with HFpEF from apical 4-chamber (A4C) view images. The method comprises steps of: (a) segmenting the plurality of A4C view images to produce a plurality of segmented images; (b) extracting a plurality of features from each segmented A4C view images of step (a) to produce a plurality of linear waveforms; and (c) training the plurality of linear waveforms of step (b) with the diagnosis of the subjects, thereby establishing the model. Also herein is a method for identifying and treating a subject having HFpEF by providing a medicament to the subject having HFpEF identified by the present model.