2021/05/27 by Xu Cao, Cao, Xu, Zijie Chen +19
Engineering · Medicine · Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Non-Invasive Vital Sign Monitoring #Retinal Imaging and Analysis #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.12951
arxiv created 2021/05/27 · openalex publication_date 2021/05/27 · arxiv updated 2021/05/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Venipucture is a common step in clinical scenarios, and is with highly practical value to be automated with robotics. Nowadays, only a few on-shelf robotic systems are developed, however, they can not fulfill practical usage due to varied reasons. In this paper, we develop a compact venipucture robot -- VeniBot, with four parts, six motors and two imaging devices. For the automation, we focus on the positioning part and propose a Dual-In-Dual-Out network based on two-step learning and two-task learning, which can achieve fully automatic regression of the suitable puncture area and angle from near-infrared(NIR) images. The regressed suitable puncture area and angle can further navigate the positioning part of VeniBot, which is an important step towards a fully autonomous venipucture robot. Validation on 30 VeniBot-collected volunteers shows a high mean dice coefficient(DSC) of 0.7634 and a low angle error of 15.58° on suitable puncture area and angle regression respectively, indicating its potentially wide and practical application in the future.