Mobile Imaging for Surgical Tray Item Enumeration
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Solution Overview
Problem
Current inventory tracking methods in healthcare settings, particularly in operating rooms, are prone to errors and time-consuming due to manual enumeration, which can delay procedures and pose safety risks, and existing intelligent systems lack the precision needed for per-item enumeration.
Innovation Solution
An imaging and tracking device captures images of inventory items within containers, using sensors and machine learning to detect presence or absence, identify items, and adjust its field of view, generating alerts for inventory management systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual enumeration of inventory items is used, then device complexity is reduced, but measurement precision and productivity deteriorate
Solution Approach 1:
The patent replaces manual mechanical counting with an automated imaging and tracking device that uses image sensors, processors, and machine learning algorithms to detect, enumerate, and track inventory items. The system captures images, processes them through neural networks, and automatically determines item presence, absence, and quantities without human intervention.
Solution Approach 2:
The patent introduces an imaging and tracking device as an intermediary between the inventory items and the inventory management system. This device acts as a mediator that captures visual data, processes it through image analysis algorithms, and translates it into structured inventory information for the management system.
2Device complexity
If manual enumeration of inventory items is used, then device complexity is reduced, but productivity deteriorates
Solution Approach 1:
The patent replaces slow manual counting processes with automated imaging and machine learning-based enumeration. The system can capture multiple images simultaneously, process them through parallel computing architectures, and generate inventory reports in real-time or near-real-time, dramatically increasing tracking speed.
Solution Approach 2:
The patent performs preliminary actions by capturing images of inventory items before formal processing occurs. The system pre-processes images through detection algorithms, identifies items of interest, and prepares data structures in advance, enabling rapid final enumeration and reporting when needed.
3Extent of automation
If conventional intelligent inventory systems are used, then automation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the inventory tracking problem into distinct processing stages: image capture, pre-processing, detection, enumeration, and verification. Each stage is handled by specialized algorithms and components, allowing the system to maintain high automation while achieving precise per-item enumeration through focused, specialized processing at each level.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors its own performance, compares detected items against expected inventory lists, and adjusts its detection parameters accordingly. This closed-loop approach enables the automated system to self-correct and maintain high precision in enumeration.
Data Source
AI summary
A method that includes capturing, by an imaging and tracking device, an image of a container and performing, by a processor in communication with the imaging and tracking device, image analysis on the image captured by the imaging and tracking device. The method also includes determining, based on the image analysis, one or more of a presence or an absence of inventory items with respect to the container. Responsive to determining the presence of the inventory items, the method includes identifying the inventory items that are contained within the container. Responsive to determining the absence of the inventory items, the method includes identifying the inventory items that are absent from the container.