Video Laryngoscope Blade Detection via Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video laryngoscopes face challenges in optimizing viewing of patient anatomy during intubation due to variations in blade types, which affect light refraction and camera obfuscation, leading to suboptimal image quality and difficulties in tracking blade usage and inventory.
Innovation Solution
The implementation of a video laryngoscope system that includes blade detection via image analysis, using machine-learning models to identify blade size and curvature from captured images, allowing for automatic adjustment of camera and lighting settings and transmission of blade identification for record-keeping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different blade types are used to accommodate different patient anatomy, then adaptability is improved, but image quality deteriorates due to light refraction and camera obfuscation
Solution Approach 1:
The system dynamically adjusts camera and lighting settings based on the detected blade type. The processor automatically modifies exposure time, gain, and lighting intensity in real-time according to the optical properties of each blade, enabling optimal image quality across different blade configurations without manual intervention
Solution Approach 2:
The system changes multiple parameters simultaneously including camera exposure time, gain settings, and lighting intensity based on the detected blade type. These parameter adjustments compensate for the varying light refraction and obfuscation characteristics of different blades, maintaining consistent image quality across diverse blade configurations
2Device complexity
If manual tracking of blade usage is implemented, then inventory management is simplified, but time consumption increases
Solution Approach 1:
The system automatically performs blade identification and record-keeping without requiring manual intervention. The processor detects the blade type through image analysis, automatically logs the blade usage information, and updates inventory records, enabling the system to serve itself in terms of data collection and documentation
Solution Approach 2:
The manual mechanical process of tracking blade usage is replaced with an automated optical and computational system. The camera captures blade images, the processor analyzes them using image recognition algorithms, and the system automatically updates digital records, substituting manual paperwork with automated digital tracking
3Productivity
If automatic blade detection is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The camera serves multiple functions: it captures images for clinical visualization and simultaneously captures images for blade identification. The processor also performs dual roles by analyzing both the clinical view and the blade characteristics, enabling one system to accomplish multiple tasks without adding separate dedicated components
Solution Approach 2:
The system uses an intermediary image capture approach where the camera captures blade images that serve as the basis for automatic identification. This intermediary step enables the processor to derive blade type information without requiring direct physical interaction or complex sensing mechanisms
Data Source
AI summary
A video laryngoscope with blade identification is disclosed. The video laryngoscope may be capable of identifying a blade that has been coupled to the video laryngoscope. Identification of the blade may be based on an image from a camera of the video laryngoscope. The blade identification may be automatic. Blade identification may be performed via image recognition rules and/or machine learning (ML) models. Additionally, an image may be the only information used to determine blade identification. Based on the blade identification, various settings of the video laryngoscope may be adjusted (e.g., lighting or camera settings). Determined blade identification may be sent to a facility computer for maintaining patient records, procedure records, and/or inventory. Additionally, blade identification may be used in determining a video classification of intubation (VCI) score.