Data-Driven Modelling and Control for Robot Needle Insertion in Deep Anterior Lamellar Keratoplasty

William Edwards, Gao Tang, Yuan Tian, Mark Draelos, Joseph Izatt, Anthony Kuo, Kris Hauser

Research output: Contribution to journalArticlepeer-review

Abstract

Deep anterior lamellar keratoplasty (DALK) is a technique for cornea transplantation which is associated with reduced patient morbidity. DALK has been explored as a potential application of robot microsurgery because the small scales, fine control requirements, and difficulty of visualization make it very challenging for human surgeons to perform. We address the problem of modelling the small scale interactions between the surgical tool and the cornea tissue to improve the accuracy of needle insertion, since accurate placement within 5% of target depth has been associated with more reliable clinical outcomes. We develop a data-driven autoregressive dynamic model of the tool-tissue interaction and a model predictive controller to guide robot needle insertion. In an ex vivo model, our controller significantly improves the accuracy of needle positioning by more than 40% compared to prior methods.

Original languageEnglish (US)
Pages (from-to)1526-1533
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume7
Issue number2
DOIs
StatePublished - Apr 2022

Keywords

  • Medical robots and systems
  • Model learning for control
  • Surgical robotics: planning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence

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