Smart 3D PACS Workflow Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Picture Archiving and Communication Systems (PACS) face challenges in dynamically adapting hanging protocols to user preferences and varying data complexities, leading to inefficiencies in image display and diagnostic processes.
Innovation Solution
Implementing a machine learning-based system that automatically identifies and applies previously learned hanging protocols with three-dimensional manipulation, allowing for personalized and adaptive image display configurations based on user interactions and data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current PACS systems use general hanging protocols to format display of images, then images can be displayed based on modality, anatomy, and procedure, but the system cannot dynamically adapt to user preferences and varying data complexities
Solution Approach 1:
The system automatically learns and applies hanging protocols by monitoring user interactions and analyzing image data characteristics. The machine learning module enables the PACS system to self-configure display protocols without requiring manual intervention or complex user input, allowing it to adapt to individual user preferences and data complexity automatically
Solution Approach 2:
The system dynamically adjusts hanging protocol parameters such as image orientation, grouping, and display layout based on analyzed data characteristics and user behavior patterns. The machine learning model continuously refines protocol parameters to optimize image display for different modalities, anatomies, and procedures
2Productivity
If manual hanging protocol configuration is used, then users can customize display settings, but user productivity is reduced due to time-consuming setup processes
Solution Approach 1:
The system performs preliminary learning and configuration of hanging protocols before the user needs to review images. By monitoring user interactions with previous exams and pre-configuring optimal display settings, the system eliminates the need for users to manually set up protocols during their workflow
Solution Approach 2:
The machine learning module automatically performs protocol configuration based on learned patterns from user behavior and image data characteristics, freeing users from manual setup tasks and enabling them to focus directly on diagnostic review
3Adaptability or versatility
If default display protocols are applied to all images, then a standard workflow is maintained, but the system cannot accommodate varying data complexities and user-specific preferences
Solution Approach 1:
The system transitions from static default protocols to dynamic, adaptive protocols that automatically adjust based on real-time analysis of image data characteristics and user interaction patterns. The machine learning module enables protocols to evolve and optimize for each user's specific needs while maintaining workflow consistency
Data Source
AI summary
Methods and systems to provide a hanging protocol including three-dimensional manipulation for display of clinical images in an exam are disclosed. An example method includes detecting selection of a new image exam for display by a user. The example method includes automatically identifying at least one of a) a previously learned hanging protocol saved for the user and b) a saved hanging protocol associated with a prior image exam corresponding to the new image exam. The example method includes applying the saved hanging protocol to the new image exam, the saved hanging protocol including three-dimensional manipulation to be automatically applied to the new image exam as part of the hanging protocol configuration for display. The example method includes facilitating display of the new image exam based on the saved hanging protocol.