Vision-Based Mammography Workflow Monitoring
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Solution Overview
Problem
Current mammography and biopsy procedures face inefficiencies due to manual setup and potential user errors, requiring extensive hardware sensors and time-consuming configurations for accessory detection and workflow management.
Innovation Solution
An x-ray mammography system equipped with a vision sensing system using cameras to detect accessories and monitor workflow steps, automatically adjusting user interface inputs and system operations to optimize procedures and prevent errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual setup and hardware sensors are used for accessory detection, then system reliability is improved, but device complexity and time consumption increase
Solution Approach 1:
The patent replaces mechanical hardware sensors with a vision-based system using cameras and image processing algorithms to detect accessories and monitor workflow. The vision system captures images, processes them through machine learning models, and automatically identifies accessories, replacing the need for physical sensor installations while maintaining detection accuracy.
Solution Approach 2:
The system creates a digital representation of the physical workspace through vision sensing. Cameras capture visual information about accessories and workflow steps, which are then processed to generate a digital model of the current system state. This digital copy enables automated decision-making without requiring physical sensors to directly interact with each component.
2Measurement precision
If hardware sensors are deployed for accessory detection, then measurement precision is improved, but loss of time and device complexity increase
Solution Approach 1:
The system pre-trains machine learning models with extensive datasets of accessory images before deployment. During actual workflow, the pre-trained models can immediately recognize accessories without requiring real-time sensor calibration or setup. The vision system is pre-configured to understand various accessory types, eliminating time-consuming configuration steps during each procedure.
Solution Approach 2:
The patent replaces time-consuming manual configuration and sensor setup with automated vision-based detection. The system continuously captures and processes visual data in real-time, automatically identifying accessories and updating workflow status without requiring manual intervention or sensor calibration, significantly reducing configuration time.
3Ease of operation
If manual workflow management is used, then ease of operation is maintained, but productivity and error rate worsen
Solution Approach 1:
The system automatically monitors workflow steps and detects accessories without requiring manual input from operators. The vision system self-adjusts by capturing images, processing them through AI algorithms, and automatically updating the workflow status. This self-service capability increases productivity while maintaining ease of operation, as the system handles complex monitoring tasks autonomously.
Solution Approach 2:
The system continuously provides feedback to operators through automated workflow monitoring and status updates. The vision system detects current workflow steps and accessory presence, then communicates this information back to the control system and operators, enabling real-time adjustments and reducing errors. This feedback loop improves productivity by preventing mistakes before they occur.
4Reliability
If extensive hardware sensors are used for workflow monitoring, then reliability is improved, but loss of time and device complexity increase
Solution Approach 1:
The vision system serves multiple functions simultaneously: it detects accessories, monitors workflow steps, captures procedural documentation, and provides training data for AI models. This single multi-functional system replaces what would otherwise require multiple specialized hardware sensors and systems, reducing both setup time and device complexity while maintaining comprehensive monitoring capability.
Data Source
AI summary
Various methods and systems are provided for workflow monitoring during x-ray mammography and related procedures. In one example, a vision system is utilized to monitor an x-ray mammography system, accessories associated with the system, and surrounding environment. Based on the detection and user indications, via a user interface for example, one or more of a current mode of operation of the x-ray system, a current workflow step in the current mode, and one or more errors may be identified using the vision system, and one or more of indications to the user and system adjustments may be performed based on the identification.