Orthopaedic Surgical Tray Inspection Using AI Layout Verification
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Solution Overview
Problem
Current manual inspection methods for orthopaedic surgical trays are labor-intensive, time-consuming, and costly, lacking the efficiency and accuracy required for reliable quality control in manufacturing and supply chain processes.
Innovation Solution
An automated visual inspection system utilizing a user interface adapter, recognition engine, recognition post-processor, and tray layout verifier, powered by advanced computer vision technologies like YOLOv7 and LoFTR, to accurately identify and verify the layout of surgical tray components, enabling rapid and reliable inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection methods are used for orthopaedic surgical trays, then operators can examine components, but the process is labor-intensive, time-consuming, and costly
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer vision system using deep learning models (Faster-RCNN, YOLO) and image processing algorithms. The system captures images of surgical trays and automatically detects, classifies, and verifies component presence and positioning, eliminating the need for manual visual examination and significantly reducing inspection time while maintaining high accuracy
Solution Approach 2:
The inspection system performs self-verification through automated image analysis and comparison against reference layouts. The deep learning models independently identify components, determine their positions, and verify compliance with specifications without requiring human intervention, enabling the system to serve itself in the inspection process
2Reliability
If manual inspection is performed to ensure component presence and placement, then quality control can be maintained, but the process lacks efficiency and scalability
Solution Approach 1:
The patent replaces manual quality control with an automated computer vision system that uses deep learning models (Faster-RCNN, YOLO) and image processing to detect, classify, and verify surgical tray components. The system captures images, processes them through multiple algorithms, and automatically determines component presence and positioning accuracy, maintaining high reliability while dramatically improving inspection efficiency and scalability
Solution Approach 2:
The system implements feedback mechanisms by comparing detected component positions and identities against reference layouts and specifications. The inspection results are automatically evaluated, and the system provides feedback on compliance status, enabling continuous quality control and allowing for rapid reinspection if defects are detected
3Extent of automation
If traditional computer vision methods are used for object detection, then inspection can be automated, but accuracy and speed are insufficient compared to deep learning approaches
Solution Approach 1:
The patent transitions from traditional computer vision methods to deep learning-based object detection by changing the algorithmic parameters and approaches. Specifically, it employs advanced models like Faster-RCNN with Feature Pyramid Networks and YOLOv3 with spatial pyramid pooling, along with non-maximum suppression techniques, to significantly improve detection accuracy and speed while maintaining high automation levels
Solution Approach 2:
The system performs preliminary actions by pre-training deep learning models on extensive datasets of surgical tray images and component variations. This pre-training enables the models to quickly and accurately detect and classify components during actual inspection, improving both detection accuracy and processing speed compared to traditional methods
Data Source
AI summary
A system for automated surgical instrument tray inspection includes a user device in communication with an inspection device. The user device captures a test image of an instrument tray being inspected and sends the image to the inspection device. The inspection device determines a unique tray identifier of the instrument tray. The inspection device generates object predictions from the test image with a trained object recognition model, post-processes the object predictions with non-max suppression based on a predetermined tray configuration associated with the tray identifier, and determines whether the object predictions match a predetermined tray layout associated with the tray identifier. The user device displays a user interface indicating whether the object predictions match the predetermined tray layout. Other embodiments are described and claimed.