Optical Coherence Tomography-Guided Robotic Ophthalmic Microsurgery via Reinforcement Learning from Demonstration

Brenton Keller, Mark Draelos, Kevin Zhou, Ruobing Qian, Anthony N. Kuo, George Konidaris, Kris Hauser, Joseph A. Izatt

Research output: Contribution to journalArticlepeer-review

Abstract

Ophthalmic microsurgery is technically difficult because the scale of required surgical tool manipulations challenge the limits of the surgeon's visual acuity, sensory perception, and physical dexterity. Intraoperative optical coherence tomography (OCT) imaging with micrometer-scale resolution is increasingly being used to monitor and provide enhanced real-time visualization of ophthalmic surgical maneuvers, but surgeons still face physical limitations when manipulating instruments inside the eye. Autonomously controlled robots are one avenue for overcoming these physical limitations. In this article, we demonstrate the feasibility of using learning from demonstration and reinforcement learning with an industrial robot to perform OCT-guided corneal needle insertions in an ex vivo model of deep anterior lamellar keratoplasty (DALK) surgery. Our reinforcement learning agent trained on ex vivo human corneas, then outperformed surgical fellows in reaching a target needle insertion depth in mock corneal surgery trials. This article shows the combination of learning from demonstration and reinforcement learning is a viable option for performing OCT-guided robotic ophthalmic surgery.

Original languageEnglish (US)
Article number9069310
Pages (from-to)1207-1218
Number of pages12
JournalIEEE Transactions on Robotics
Volume36
Issue number4
DOIs
StatePublished - Aug 2020

Keywords

  • Deep learning in robotics and automation
  • learning from demonstration
  • medical robots and systems
  • microsurgery

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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