Leipheimer Josh, Balter Max, Chen Alvin, Yarmush Martin
Rutgers University, Piscataway, NJ 08854.
J Med Device. 2022 Jun 1;16(2):021015. doi: 10.1115/1.4053688. Epub 2022 Mar 2.
Medical robots provide enhanced dexterity, vision, and safety for a broad range of procedures. In this article, we present a handheld, robotic device capable of performing peripheral catheter insertions with high accuracy and repeatability. The device utilizes a combination of ultrasound imaging, miniaturized robotics, and machine learning to safely and efficiently introduce a catheter sheath into a peripheral blood vessel. Here, we present the mechanical design and experimental validation of the device, known as VeniBot. Additionally, we present results on our ultrasound deep learning algorithm for vessel segmentation, and performance on tissue-mimicking phantom models that simulate difficult peripheral catheter placement. Overall, the device achieved first-attempt success rates of 97 ± 4% for vessel punctures and 89 ± 7% for sheath cannulations on the tissue mimicking models (n = 240). The results from these studies demonstrate the viability of a handheld device for performing semi-automated peripheral catheterization. In the future, the use of this device has the potential to improve clinical workflow and reduce patient discomfort by assuring a safe and efficient procedure.
医疗机器人为广泛的手术提供了更高的灵活性、视觉能力和安全性。在本文中，我们展示了一种手持机器人设备，它能够高精度且可重复地进行外周导管插入操作。该设备利用超声成像、小型机器人技术和机器学习的组合，将导管鞘安全有效地插入外周血管。在此，我们展示了名为VeniBot的该设备的机械设计和实验验证。此外，我们展示了用于血管分割的超声深度学习算法的结果，以及在模拟困难外周导管放置的组织模拟体模模型上的性能。总体而言，在组织模拟模型上（n = 240），该设备进行血管穿刺的首次尝试成功率为97±4%，导管鞘插管的成功率为89±7%。这些研究结果证明了一种用于执行半自动外周导管插入术的手持设备的可行性。未来，使用该设备有可能通过确保安全高效的操作来改善临床工作流程并减轻患者不适。
