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

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.

Automated summary

Deep anterior lamellar keratoplasty (DALK) is a technique for cornea transplantation that reduces patient morbidity. Robot microsurgery has been explored as a potential application for DALK due to its challenging nature for human surgeons. In this study, we have developed a data-driven autoregressive dynamic model and a model predictive controller to improve the accuracy of needle insertion during the surgery. Our experiments show that our controller significantly improves needle positioning accuracy compared to previous methods.