Intelligent lumbar anesthesia puncture guiding method and device
Through the combination of deep neural network models and sensor arrays, accurate prediction and real-time adjustment of the spinal anesthesia puncture path are achieved, solving the problem of difficult spinal anesthesia puncture positioning and improving the puncture success rate and patient safety.
CN120616703APending Publication Date: 2025-09-12TONGJI HOSPITAL ATTACHED TO TONGJI MEDICAL COLLEGE HUAZHONG SCI TECH
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Patent Information
- Application Number
- CN202510526412.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-25
- Publication Date
- 2025-09-12
AI Technical Summary
Technical Problem
In the existing technology, it is difficult to accurately locate the puncture point during spinal anesthesia puncture, and there is a risk that the puncture needle may fail to enter the correct position, which may cause problems such as piercing the arachnoid membrane and affect patient safety.
Method used
A deep neural network model is used to predict the puncture path. Combined with imaging images and previous medical history information, the sensor array monitors pressure data in real time and adjusts the puncture path to accurately locate the target tissue layer.
Benefits of technology
It improves the accuracy and success rate of spinal anesthesia puncture, reduces operation time, reduces patient discomfort, and improves safety and patient experience.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention provides an intelligent lumbar anesthesia puncture guiding method and device, which are applied to a lumbar anesthesia puncture needle, and the method comprises the following steps: obtaining case information of a puncture object, and preprocessing the case information; the preprocessed case information is input into a trained puncture prediction model, a predicted puncture path is obtained, and the predicted puncture path comprises a predicted puncture point position, the predicted puncture depth of each tissue layer, a predicted pressure threshold value and a predicted puncture angle; guiding a spinal anesthesia puncture needle to puncture according to the predicted puncture path; wherein the puncture prediction model is specifically a deep neural network model, and the puncture prediction model is obtained by training an initial model through sample case information marked with a corresponding puncture path prediction result in advance. In combination with the artificial intelligence model, the puncture path is individually recommended based on patients of different ages and body types, the accuracy of the puncture point location and the success rate of one-time puncture are improved, the operation time is shortened, and the patient experience and safety are improved.
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