Epidural Needle Depth Recognition Using SVM Classification
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Solution Overview
Problem
Current methods for performing epidural anesthesia, such as the loss of resistance technique, are subjective and operator-dependent, leading to a high failure rate and complications due to the lack of objective guidance for needle placement in patients with structural abnormalities or obesity.
Innovation Solution
A method and system that utilize tomographic images to obtain characteristic values through texture feature analysis, employing a classification boundary defined by a Support Vector Machine method to automatically recognize the axial depth and determine if the puncturing end has reached the target depth, thereby providing objective guidance for needle placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional blind needle insertion technique is used, then the operation simplicity is maintained, but the reliability and success rate deteriorate due to operator dependence and lack of objective guidance
Solution Approach 1:
The patent replaces the traditional mechanical blind needle insertion technique with an optical imaging system (ultrasound or MRI) combined with automated image processing algorithms. The system captures real-time images of the needle insertion process, processes them through texture analysis and classification algorithms, and provides automated feedback to guide needle placement into the epidural space, thereby replacing subjective mechanical palpation with objective optical detection and analysis.
Solution Approach 2:
The system performs self-diagnosis and self-guidance by automatically analyzing the captured images, classifying tissue structures, and determining needle position without requiring external expert intervention. The automated classification algorithm independently processes the imaging data and provides real-time feedback, enabling the system to guide the procedure without continuous operator expertise.
2Measurement precision
If automated image-based classification system is implemented, then the measurement precision and reliability improve, but the device complexity and cost increase
Solution Approach 1:
The patent extracts and analyzes only the essential texture features from the captured images that are relevant to needle position detection. By focusing on specific texture characteristics rather than processing the entire image data, the system achieves high measurement precision while reducing computational complexity and processing requirements.
Solution Approach 2:
The system transforms the complex image data into simplified characteristic values through texture analysis and classification. By changing the parameter representation from raw pixel data to extracted texture features and classification categories, the system achieves precise measurement while reducing the complexity of data processing and interpretation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces the risk of complications by providing real-time, objective feedback on needle placement, ensuring accurate positioning of the epidural space and improving the success rate of epidural anesthesia procedures.
Implementation Method 1
obtaining a real time tomographic image from the axial depth of a tissue where a puncturing end is
Data Source
AI summary
The present invention provides a method of obtaining a classification boundary, to limit an axial depth in a puncturing operation. The following steps of method comprises: At first, obtaining a plurality of tomographic images from the axial depth of a tissue is performed. Then, obtaining a plurality of characteristic values from the tomographic images, the characteristic values are classified by a Support Vector Machine method. A classification boundary will be obtained through a distribution of the graph for defining a specific compartment of the tissue. In addition, an automatic recognition method and system using the above mentioned method are also disclosed in the present invention.