Ultrasound Button Sequence Prediction Triggered by Probe Motion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current ultrasound imaging systems lack user-specific configurability and intelligence to suggest button sequences for efficient exam workflows, leading to suboptimal performance for both novice and expert users.
Innovation Solution
A method and system that analyze user button push sequences and probe motion to predict and output macro buttons for next actions, adapting to individual user preferences and clinical applications using predictive models and AI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ultrasound imaging systems use general workflow solutions, then they can serve all users, but they cannot adapt to user-specific preferences and workflows
Solution Approach 1:
The system automatically analyzes user behavior patterns and generates personalized macro buttons without requiring manual configuration. The system serves itself by learning from user interactions and adapting the interface automatically, eliminating the need for users to manually configure complex settings while still providing user-specific customization.
Solution Approach 2:
The system pre-configures macro buttons based on predicted user needs before the user actually needs them. By analyzing historical button push sequences and probe motion patterns, the system prepares personalized workflow shortcuts in advance, making the system adaptable without requiring complex real-time configuration decisions.
2Extent of automation
If ultrasound imaging systems manually log and store button push sequences, then they capture workflow information, but they cannot provide intelligent predictions or suggestions
Solution Approach 1:
The system continuously monitors user interactions with the control interface and probe movements, using this feedback to refine predictions of future button push sequences. This closed-loop feedback mechanism enables the system to learn from actual user behavior and improve workflow automation while preserving and utilizing workflow intelligence rather than simply logging it.
Solution Approach 2:
The patent replaces manual workflow analysis and prediction methods with machine learning algorithms and predictive models. Instead of relying on predefined rules or manual configuration, the system uses computational intelligence to analyze button push sequences and probe motion patterns, transforming mechanical data collection into intelligent workflow prediction and automation.
3Productivity
If ultrasound imaging systems use fixed control interfaces, then they are simple to operate, but they cannot speed up workflows through predictive suggestions
Solution Approach 1:
The system segments the control interface by creating dynamic macro buttons that group frequently used button sequences together. This segmentation allows the interface to present simplified, context-relevant controls based on predicted user needs, speeding up workflows by reducing the number of individual button presses required while maintaining ease of operation through intuitive, behavior-based organization.
Solution Approach 2:
The control interface transitions from a static, fixed layout to a dynamic system that adapts in real-time based on user behavior patterns and probe motion. The macro buttons are dynamically generated and updated based on predicted workflow needs, allowing the interface to optimize for productivity while preserving ease of operation through adaptive, context-aware control organization.
Data Source
AI summary
A method for performing an ultrasound examination includes obtaining sequences of button pushes performed by a user via a control interface (125) during an exam workflow (S212); differentiating components of the exam workflow based on the button pushing sequences (S213); performing sequence pattern mining of the button pushing sequences based on the differentiated components to extract user specific workflow trends, which include most frequently used button pushing sequences (S214); detecting probe motion of a transducer probe (160) during the ultrasound examination (S215); predicting strings of next button pushes using a predictive model based on at least one previous button push in the user specific workflow trends, where predicting the strings of the next button pushes is triggered by the detected probe motion (S216); and outputting macro buttons corresponding to the predicted strings of next button pushes on the control interface, wherein selection of the macro buttons executes the corresponding predicted strings of next button pushes (S217).