ABSTRACT
Intramuscular (IM) injection is mainly performed manually at present. Large-scale COVID-19 vaccination has exposed various problems of manual IM injection. In addition, the clinical success rate of manual IM injection is also unsatisfactory. Using robotic intramuscular injection system (RIMIS) is expected to realize automated vaccination and improve the success rate of IM injection. The existing robotic needle insertion system based on image guidance is not a practical option for IM injection because of the time-consuming medical imaging process. In this paper, an optical guidance method for RIMIS is proposed, which uses near-infrared optical tracking system and retro-reflective patch to achieve rapid acquisition of surface normal vector. A closed loop formed by six coordinate systems is used to realize the accurate control of the injection angle and depth. Experimental results show that the RIMIS based on the proposed method can complete the simulated IM injection operation without image guidance and possess accurate control of the injection angle and depth. © 2022 IEEE.
ABSTRACT
Medical professionals have started favoring the use of non-contact intravenous injection robots owing to their importance during the COVID-19 outbreak. However, there are currently few studies considering the robot's needle insertion angle, and most of the needle insertion operations are performed at a steep angle. This increases the rate of puncture failure, and sometimes causes significant pain in patients depending on their individual differences. Therefore, the intravenous injection of the dorsal hand is performed in this study to investigate the determination of the robot's needle insertion angle. with a focus on the optimization of the measurement data to ensure accuracy in the calculation of the needle insertion angle. First, the space point cloud of the needle insertion area on the dorsal hand is obtained by combining a monocular camera with the linear structured light scanning method , and the dorsal hand plane is obtained via fitting dorsal hand point clouds using the least squares method. During the calibration process for the linear structured light system , the measurement error is eliminated by formulating an error function and using the optimization method to iteratively solve it. Subsequently. the needle insertion angle is determined based on the obtained needle insertion area plane. Finally, experiments are conducted for the accuracy verification of the proposed method. Based on the experimental results, the average error in the optimized structured light plane position is approximately 0. 1 mm, and this serves as a foundation for subsequent automatic injection studies.
ABSTRACT
Understanding the underlying pathology in different tissues and organs is crucial when fighting pandemics like COVID-19. During conventional autopsy, large tissue sample sets of multiple organs can be collected from cadavers. However, direct contact with an infectious corpse is associated with the risk of disease transmission and relatives of the deceased might object to a conventional autopsy. To overcome these drawbacks, we consider minimally invasive autopsies with robotic needle placement as a practical alternative. One challenge in needle based biopsies is avoidance of dense obstacles, including bones or embedded medical devices such as pacemakers. We demonstrate an approach for automated planning and visualising suitable needle insertion points based on computed tomography (CT) scans. Needle paths are modeled by a line between insertion and target point and needle insertion path occlusion from obstacles is determined by using central projections from the biopsy target to the surface of the skin. We project the maximum and minimum CT attenuation, insertion depth, and standard deviation of CT attenuation along the needle path and create two-dimensional intensity-maps projected on the skin. A cost function considering these metrics is introduced and minimized to find an optimal biopsy needle path. Furthermore, we disregard insertion points without sufficient room for needle placement. For visualisation, we display the color-coded cost function so that suitable points for needle insertion become visible. We evaluate our system on 10 post mortem CTs with six biopsy targets in abdomen and thorax annotated by medical experts. For all patients and targets an optimal insertion path is found. The mean distance to the target ranges from (49.9 ± 12.9)mm for the spleen to (90.1 ± 25.8)mm for the pancreas. © 2021 by Walter de Gruyter Berlin/Boston.