Medical Tray Image Recognition for Missing Instrument Verification
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Solution Overview
Problem
Current methods for verifying the completeness of surgical instrument trays rely heavily on human attention and are prone to errors, leading to potential delays and increased costs due to missing items, with existing technologies like RFID tags facing challenges such as high costs and inability to withstand sterilization temperatures.
Innovation Solution
Implementing computer vision models, specifically trained on medical instrument trays, to identify and verify the presence or absence of instruments using image processing and machine learning, capable of handling fine-grained segmentation and real-time verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If RFID tags are used to track instruments, then inventory verification capability is improved, but cost and device complexity increase significantly
Solution Approach 1:
The patent replaces RFID tags and readers with a computer vision system using cameras and image processing algorithms. This substitution eliminates the need for electronic tags and readers, reducing device complexity while maintaining inventory verification capability through optical detection and machine learning-based instrument recognition.
Solution Approach 2:
The system creates visual copies (images) of the instrument tray and its contents, then processes these copies through image recognition algorithms to verify instrument presence. This approach avoids physical tags on instruments while still enabling accurate inventory tracking through digital representation and analysis.
2Reliability
If RFID tags are used to track instruments, then inventory verification capability is improved, but cost increases due to tag and hardware requirements
Solution Approach 1:
The patent uses standard, inexpensive cameras instead of expensive RFID tags and readers. The system leverages readily available imaging technology and software-based processing, eliminating the need for costly specialized hardware components while achieving comparable or superior verification accuracy.
Solution Approach 2:
The patent replaces the expensive RFID hardware ecosystem with a computer vision approach using standard cameras and software algorithms. This substitution dramatically reduces component costs while maintaining or improving inventory verification reliability through advanced image processing and machine learning techniques.
3Device complexity
If manual verification methods are used, then device complexity is reduced, but human error increases leading to missing items
Solution Approach 1:
The system enables self-service verification where the computer vision automatically detects, identifies, and verifies instruments without human intervention. The image recognition model independently analyzes tray contents, compares against expected inventory, and flags discrepancies, eliminating reliance on human attention and training while maintaining simple overall system architecture.
Solution Approach 2:
The system provides automated feedback by comparing detected instruments against the expected tray configuration and notifying users of missing or incorrect items. This feedback mechanism eliminates human error in verification while keeping the system relatively simple by using rule-based comparison and alert generation rather than complex control systems.
4Device complexity
If inventory sheets and pictures are used for verification, then device complexity is reduced, but human error remains high due to reliance on operator attention
Solution Approach 1:
The system replaces human operators with automated computer vision that independently performs verification without requiring operator attention or interpretation. The image recognition model consistently identifies instruments and compares them against expected configurations, eliminating variability in human performance while maintaining simple visual-based verification approach.
Solution Approach 2:
The patent substitutes human visual inspection with computer-based image processing and recognition. This replacement maintains the simplicity of visual verification methods while eliminating human error and inconsistency, achieving reliable automated detection through algorithms that process images objectively and repeatably.
Data Source
AI summary
Systems, methods, and computer-readable storage media for scanning medical instrument trays to determine which items are present and which are missing. This is accomplished by receiving, prior to an event, pre-event media content capturing a medical tray, then executing a model trained to recognize instruments on the medical tray, resulting in a list of pre-event found medical instruments. After the event, post-event media content capturing the medical tray is received, and the model executed again, resulting in a list of post-event found medical instruments. The list of pre-event found medical instruments is compared against the list of post-event found medical instruments, resulting in a comparison, and the results displayed.