Deep learning robotic guidance for autonomous vascular access

Alvin I. Chen, Max L. Balter, Timothy J. Maguire, Martin L. Yarmush

Research output: Contribution to journalArticlepeer-review

Abstract

Medical robots have demonstrated the ability to manipulate percutaneous instruments into soft tissue anatomy while working beyond the limits of human perception and dexterity. Robotic technologies further offer the promise of autonomy in carrying out critical tasks with minimal supervision when resources are limited. Here, we present a portable robotic device capable of introducing needles and catheters into deformable tissues such as blood vessels to draw blood or deliver fluids autonomously. Robotic cannulation is driven by predictions from a series of deep convolutional neural networks that encode spatiotemporal information from multimodal image sequences to guide real-time servoing. We demonstrate, through imaging and robotic tracking studies in volunteers, the ability of the device to segment, classify, localize and track peripheral vessels in the presence of anatomical variability and motion. We then evaluate robotic performance in phantom and animal models of difficult vascular access and show that the device can improve success rates and procedure times compared to manual cannulations by trained operators, particularly in challenging physiological conditions. These results suggest the potential for autonomous systems to outperform humans on complex visuomotor tasks, and demonstrate a step in the translation of such capabilities into clinical use.

Original languageEnglish (US)
Pages (from-to)104-115
Number of pages12
JournalNature Machine Intelligence
Volume2
Issue number2
DOIs
StatePublished - Feb 1 2020

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Artificial Intelligence

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