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Puncture site decision method for venipuncture robot based on near-infrared vision and multiobjective optimization.

TianBao He¹, ChuangQiang Guo¹, Li Jiang¹

¹State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001 China.

Science China. Technological Sciences

|December 26, 2022

Summary

This study introduces a novel method for autonomous venipuncture robots to identify optimal puncture sites using AI-driven vein segmentation and a multiobjective optimization model. This enhances robot decision-making capabilities for improved clinical application.

Keywords:

near-infrared vision puncture site decision vein extraction venipuncture robot

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A New Best Practice for Validating Tail Vein Injections in Rat with Near-infrared-Labeled Agents

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Published on: April 19, 2019

Area of Science:

Biomedical Engineering
Robotics
Artificial Intelligence

Background:

Venipuncture robots offer enhanced precision but lack autonomous decision-making for puncture site selection.
Current limitations hinder the widespread adoption of robotic venipuncture systems.

Purpose of the Study:

To develop a multi-information fusion method for determining optimal venipuncture sites, thereby increasing robot autonomy.
To enable robots to make independent decisions regarding puncture site selection for venipuncture procedures.

Main Methods:

A U-Net with a soft attention mechanism (SAU-Net) was trained on a forearm vein image dataset for accurate vein segmentation.
Near-infrared vision was used to extract vein features, including depth, diameter, curvature, and length.
A multiobjective optimization model was employed to determine the optimal puncture site based on extracted vein characteristics.

Main Results:

The SAU-Net achieved a vein segmentation accuracy of 91.2% and a vein extraction rate of 86.7%.
The multiobjective optimization model provided a Pareto solution set with an average decision time of 1.458 seconds.
Real-time vein segmentation and puncture site determination were demonstrated on a venipuncture robot using a near-infrared camera.

Conclusions:

The proposed method significantly enhances the autonomy of venipuncture robots by enabling intelligent puncture site selection.
This approach holds the potential to dramatically improve the implementation and effectiveness of robotic venipuncture in clinical settings.