Medical Tool Recognition Assembly for Insertion Signature Detection
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Solution Overview
Problem
Existing minimally invasive medical procedures face challenges in properly installing and recognizing medical instruments, which can lead to safety issues and inefficiencies due to the lack of effective detection and recognition systems.
Innovation Solution
A tool recognition assembly is employed to detect the presence, absence, and generate insertion signatures for medical tools using various sensor types, allowing for proper installation and recognition through comparison with pre-determined models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a tool recognition assembly with sensor data acquisition and insertion signature generation is implemented, then tool installation verification and recognition accuracy are improved, but device complexity increases
Solution Approach 1:
The system pre-generates insertion signatures for different tool types before actual tool installation. These signatures represent expected sensor data patterns for properly installed tools. During operation, the system simply compares real-time sensor data against these pre-established signatures to verify correct tool installation, eliminating the need for complex real-time analysis algorithms.
Solution Approach 2:
The system creates digital copies (insertion signatures) of the expected sensor data patterns for each tool type. These signatures are stored in memory and used as reference templates. When a tool is installed, the actual sensor readings are compared against the corresponding digital copy to verify proper installation, simplifying the verification process while maintaining high accuracy.
2Reliability
If multiple sensor types and detection zones are used to detect tool presence and characteristics, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The detection system is divided into multiple independent detection zones, each with its own sensor or sensor array. Each detection zone monitors specific characteristics (presence, orientation, depth) independently. This segmentation allows the system to achieve high reliability through multiple independent verification points while keeping each individual sensor simple and manageable.
Solution Approach 2:
The sensor system is designed so that the same physical sensors serve multiple detection functions. For example, electromagnetic sensors detect both the presence of ferromagnetic tools and their orientation simultaneously. This multi-functionality increases detection reliability without proportionally increasing the number of physical sensors required.
3Reliability
If real-time sensor data acquisition and comparison with model signatures is performed, then safety is improved by preventing improper tool use, but processing time and energy consumption increase
Solution Approach 1:
The system performs the computationally intensive signature generation and pattern recognition work in advance, creating a library of expected sensor data patterns for each tool type. During actual surgical procedures, the system only performs simple real-time comparisons against these pre-computed signatures, dramatically reducing energy consumption during critical operations while maintaining continuous safety monitoring.
Solution Approach 2:
The system uses simple threshold-based comparison algorithms and basic pattern matching techniques rather than complex machine learning models. These lightweight computational methods consume minimal energy and can be executed rapidly on embedded systems, providing continuous safety verification without significant energy overhead.
Data Source
AI summary
A method of detecting a tool being received in a medical system, the method including receiving, in a tool recognition assembly having a first reader with a first detection zone, the tool having a first target. The method also includes acquiring first sensor data from the first reader for the first detection zone and detecting an indication of an absence of the first target when the first sensor data is within a first pre-determined threshold range and logging the absence indication. The method also includes creating an insertion signature associated with the tool being received in the tool recognition assembly by combining, in a chronological sequence, the absence and presence indications from the first reader. The method further includes comparing the insertion signature to a predetermined set of model insertion signatures and determining a characteristic of the tool being received in the tool recognition assembly based on the comparing.