Cardiac Anomaly Detection Using Neural Network Analysis
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Solution Overview
Problem
Current manual analysis of ultrasound movie clips for cardiac assessments is prone to errors, requires skilled personnel, limits throughput, and often only analyzes a few frames, leaving valuable information unused.
Innovation Solution
A system and method utilizing a specifically configured computer hardware arrangement and neural networks trained on multiple recognition and analysis procedures to detect cardiac anomalies, classify their severity, and perform cardiac measurements from ultrasound imaging information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of ultrasound movie clips is performed by technician or physician, then skilled user judgment can be applied, but the chance of error increases, throughput is limited, and only several frames are fully analyzed
Solution Approach 1:
The patent replaces the manual mechanical analysis process performed by technicians and physicians with an automated computer-based system that processes ultrasound images. The system uses image processing algorithms to automatically detect cardiac structures, measure parameters, and generate reports, eliminating the need for manual frame-by-frame analysis while improving both accuracy and throughput.
Solution Approach 2:
The system enables self-service automated analysis where the computer system independently processes ultrasound clips without requiring skilled user intervention for each measurement. The automated detection and measurement capabilities allow the system to perform complete cardiac assessments autonomously, freeing technicians and physicians from repetitive manual tasks.
2Reliability
If manual analysis is performed by skilled user, then comprehensive cardiac assessment can be achieved, but time complexity increases and only several frames are fully analyzed
Solution Approach 1:
The system performs preliminary automated detection and measurement of cardiac parameters from all frames in the ultrasound clip before final interpretation. By pre-processing the entire dataset and identifying key features automatically, the system prepares comprehensive analysis results that can be quickly reviewed, reducing the time needed for final diagnosis while maintaining reliability.
Solution Approach 2:
The system continuously processes all frames in the ultrasound clip rather than analyzing only selected frames. The automated analysis maintains continuous monitoring of cardiac structures throughout the entire video sequence, ensuring no valuable information is missed while reducing overall analysis time through efficient algorithmic processing.
3Loss of information
If more frames are analyzed, then more information is utilized, but the time complexity and analysis duration increase
Solution Approach 1:
The system extracts only the most relevant features and parameters from the ultrasound frames using automated detection algorithms. By identifying and extracting key cardiac measurements (ejection fraction, chamber dimensions, wall thickness) directly from the image data, the system utilizes information from all frames efficiently without requiring manual review of every frame, thus reducing processing time while maximizing information utilization.
Data Source
AI summary
A system and method for detecting at least one cardiac anomaly includes a specifically configured computer hardware arrangement configured to receive ultrasound imaging information related to a heart of a patient and to use at least one neural network trained on multiple recognition and analysis procedures to detect at least one anomaly and/or to classify a severity of at least one anomaly.