An automatic needle insertion method combining a blood flow optimization model with a neural network

CN116584974BActive Publication Date: 2026-07-24ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG UNIV
Filing Date
2023-05-26
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

In existing technologies, medical staff have difficulty accurately locating veins or arteries, leading to delays in rescue time and multiple unnecessary needle insertions that cause pain and infection risks, especially when the subcutaneous fat and muscle layers are thick or the hair is dense.

Method used

By combining a blood flow optimization model with a neural network, the system automatically scans blood flow feedback signals using the Doppler ultrasound principle, calculates the location of blood vessels and the distribution of blood flow velocity, and uses a neural network to determine the optimal insertion point and angle, simulating professional insertion techniques to achieve automatic insertion.

Benefits of technology

It increases the survival rate of self-rescue and first aid in environments lacking professional personnel, avoids the pain of multiple injections, and the device is small, lightweight, and easy to carry.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses an automatic needle insertion method combining a blood flow optimization model with a neural network. The method first calculates the blood vessel position and blood flow velocity distribution of a needle insertion area according to blood flow information measured by automatic ultrasonic scanning by means of the Doppler ultrasonic principle, then establishes a blood flow relative velocity optimization model and a neural network with a selection function, and decides the optimal needle insertion point and needle insertion angle on the basis of physical measurement results, and finally simulates the needle insertion path by means of a neural network with a learning function according to the needle insertion method of a professional. The method can be used for needle insertion of subcutaneous blood vessels of limbs and other body parts, and overcomes the difficulty of manual needle insertion when the blood vessel is not visible to the naked eye, thereby providing technical assistance for self-rescue and first aid in an environment lacking professional personnel.
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