Anatomy-Aware Graphical User Interface Adaptation
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Solution Overview
Problem
Traditional graphical user interfaces in medical imaging are not anatomy-specific, leading to clutter and inefficiency in image interpretation, as they provide a standard set of tools for all image modalities and anatomies, which can slow down the workflow of medical professionals.
Innovation Solution
An anatomy-aware adaptation framework that detects landmarks in medical images using a trained machine learning model, infers the body section, and dynamically selects relevant user interface elements to adapt the graphical user interface, reducing clutter and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a standard set of tools and user interface elements is provided for all image modalities and anatomies, then the graphical user interface is universally applicable, but screen clutter increases and workflow speed decreases
Solution Approach 1:
The user interface elements and tools are segmented into anatomy-specific groups. The system divides the complete set of available tools into subsets relevant to specific body regions (e.g., neurology tools for head images, cardiology tools for chest images), allowing the interface to present only the necessary subset for each imaging scenario rather than all possible tools.
Solution Approach 2:
The graphical user interface dynamically adapts its composition based on the detected anatomy. The system automatically reconfigures the interface by adding or removing tools and elements in real-time according to the identified body region, transforming the interface from a static universal design to a dynamic anatomy-specific design.
2Productivity
If anatomy-specific user interface elements are provided, then workflow efficiency improves, but the complexity of the graphical user interface system increases
Solution Approach 1:
The system performs self-service by automatically detecting the anatomy in the medical image and autonomously configuring the appropriate user interface elements. This eliminates the need for manual selection of tools by the user and removes the burden of managing complex interface configurations, as the system handles the adaptation automatically based on image analysis.
Solution Approach 2:
The manual mechanical process of selecting and configuring interface elements is replaced by an automated computer vision system. The machine learning model detects anatomical landmarks and automatically triggers the appropriate interface configuration, substituting the manual selection mechanism with an automated recognition and adaptation system.
3Adaptability or versatility
If all tools are always visible, then users have access to complete functionality, but relevant tools are harder to identify and select
Solution Approach 1:
The user interface applies local quality by presenting different sets of tools and elements tailored to the specific anatomy being viewed. Each body region receives a customized interface configuration with tools and functions locally optimized for that anatomical context, rather than a uniform global interface for all scenarios.
Data Source
AI summary
A framework for anatomy-aware adaptation of a graphical user interface. Landmarks are first detected by passing one or more current images through a trained machine learning model. A body section may then be inferred based on the detected landmarks. One or more user interface elements may be determined based on the inferred body section. A graphical user interface may then be adapted with the determined one or more user interface elements.