Dynamic Medical Imaging Series Ordering via User Interaction Analysis
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Solution Overview
Problem
Current medical imaging exam interpretation methods lack efficiency and accuracy due to fixed viewing orders and importance determinations that do not account for individual user interactions or clinical specifics.
Innovation Solution
Systems and methods that dynamically determine the view order and importance of medical imaging series based on user interaction data, clinical information, and exam characteristics, using computing devices to customize the display and processing of images according to user preferences and interaction patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed viewing orders are used for medical imaging series, then system complexity is reduced and ease of operation is improved, but interpretation efficiency and accuracy deteriorate due to inability to account for individual user interactions and clinical specifics
Solution Approach 1:
The patent implements dynamic series ordering that adapts to individual user interactions and clinical contexts. The system monitors user behavior patterns, tracks which series are viewed first and longest, and automatically reorders series based on real-time interaction data. This dynamic adaptation resolves the contradiction by allowing the system to become increasingly optimized for each user without requiring manual configuration, thereby improving interpretation efficiency while keeping the interface simple.
Solution Approach 2:
The system incorporates feedback loops where user interaction data (viewing time, navigation patterns, series selection frequency) is continuously collected and used to refine series ordering. This feedback mechanism enables the system to learn from each user's preferences and clinical priorities, automatically adjusting the display order to maximize interpretation efficiency without increasing operational complexity for the end user.
2Measurement precision
If fixed viewing orders are used for medical imaging series, then ease of operation is improved, but interpretation accuracy deteriorates due to lack of personalization
Solution Approach 1:
The system provides self-service personalization by automatically analyzing user interaction patterns and configuring series ordering without requiring manual user input. The system monitors which series each user views most frequently and for longest durations, then autonomously reorders series to match individual preferences. This self-service approach improves interpretation accuracy through personalization while maintaining ease of operation, as users simply view their customized order without needing to configure it.
3Measurement precision
If interaction data collection and analysis is implemented, then series ordering accuracy is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system implements partial action by focusing on collecting and analyzing only the most relevant interaction data points (viewing time, first-series selection, frequency of return visits) rather than attempting to track every possible user action. This selective data collection approach achieves sufficient ordering accuracy to improve interpretation while avoiding the complexity overhead of comprehensive behavior analysis, resolving the contradiction between precision and processing complexity.
Data Source
AI summary
Provided herein are various systems and methods for monitoring how users interact with medical imaging exams to automatically determine the view order and importance of various series within medical imaging exams as a function of a particular user, exam type, clinical information, and/or other characteristic of medical data.