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"Considering the vascular cross-sectional area and bending of each vein injection point area, the optimal injection point of the dorsal hand vein is obtained via a comprehensive decision-making process" Zhang et al (2022).
Intravenous access intelligent robot

Abstract:

Since the emergence of new coronaviruses and their variant virus, a large number of medical resources around the world have been put into treatment. In this case, the purpose of this article is to develop a handback intravenous intelligence injection robot, which reduces the direct contact between medical staff and patients and reduces the risk of infection. The core technology of hand back intravenous intelligent robot is a handlet venous vessel detection and segmentation and the position of the needle point position decision. In this paper, an image processing algorithm based on U-Net improvement mechanism (AT-U-Net) is proposed for core technology. It is investigated using a self-built dorsal hand vein database and the results show that it performs well, with an F1-score of 93.91%. After the detection of a dorsal hand vein, this paper proposes a location decision method for the needle entry point based on an improved pruning algorithm (PT-Pruning). The extraction of the trunk line of the dorsal hand vein is realized through this algorithm. Considering the vascular cross-sectional area and bending of each vein injection point area, the optimal injection point of the dorsal hand vein is obtained via a comprehensive decision-making process. Using the self-built dorsal hand vein injection point database, the accuracy of the detection of the effective injection area reaches 96.73%. The accuracy for the detection of the injection area at the optimal needle entry point is 96.50%, which lays a foundation for subsequent mechanical automatic injection.

Reference:

Zhang G, Gao X, Zhu Z, Zhou F, Yu D. Determination of the location of the needle entry point based on an improved pruning algorithm. Math Biosci Eng. 2022 May 30;19(8):7952-7977. doi: 10.3934/mbe.2022372. PMID: 35801452.