Automated Doppler Study Classification via Neural Network Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Doppler ultrasound systems lack automation in processing acquired data due to the inability to identify the type of Doppler study, which is typically manually specified by users, leading to inefficient and inaccurate analysis of blood flow through the heart.
Innovation Solution
A Doppler study classification system utilizing neural networks to process both Doppler spectra and two-dimensional ultrasound images, inferring the study type by generating probability distributions and combining them to automatically determine the appropriate processing algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual specification of study type is used, then accuracy of Doppler data processing is maintained, but productivity and efficiency deteriorate due to time-consuming manual intervention
Solution Approach 1:
The system performs self-service by automatically inferring the study type from the acquired Doppler data and 2D ultrasound images without requiring manual user input. The neural network model autonomously analyzes the data characteristics and determines the appropriate study type, enabling the system to serve itself in the classification task.
Solution Approach 2:
The manual mechanical process of user-based study type specification is replaced with an automated electronic system using neural networks. The system substitutes human cognitive judgment with machine learning algorithms that process Doppler spectra and image features to automatically determine study type.
2Measurement precision
If automated processing is implemented without study type identification, then productivity improves, but measurement precision and reliability deteriorate due to inability to apply appropriate algorithms
Solution Approach 1:
The system performs preliminary action by inferring the study type before applying the specific processing algorithm. The neural network analyzes the Doppler data and 2D images to predict the study type in advance, which then guides the selection of the appropriate analysis algorithm, ensuring accurate processing from the outset.
Solution Approach 2:
The neural network model serves as a universal classifier that can handle multiple study types through a single system. The model is trained to recognize various Doppler study types (such as mitral valve, aortic valve, pulmonary valve studies) and automatically selects the appropriate classification, making the system multi-functional without requiring separate dedicated systems for each study type.
3Extent of automation
If study type inference using neural networks is implemented, then extent of automation improves, but device complexity increases due to additional processing requirements
Solution Approach 1:
The automation process is segmented into distinct functional modules: data acquisition from Doppler and 2D sources, neural network-based study type inference, and algorithm selection based on inferred type. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by breaking down the complex automation task into manageable stages.
Data Source
AI summary
Example apparatus, systems, and methods for image data processing are disclosed and described. An example system includes an image capturer to facilitate capture of an image. The example system includes a Doppler spectrum recorder to record a Doppler spectrum. The example system includes a study type inferrer to infer a study type associated with the Doppler spectrum by: processing the Doppler spectrum using at least one neural network to generate a first probability distribution among study type classifications; processing the image using the at least one neural network to generate a second probability distribution among the study type classifications; and combining the first probability distribution and the second probability distribution to infer a study type.