Towards Autonomous Eye Surgery by Combining Deep Imitation Learning with Optimal Control.

ABSTRACT: During retinal microsurgery, precise manipulation of the delicate retinal tissue is required for positive surgical outcome. However, accurate manipulation and navigation of surgical tools remain difficult due to a constrained workspace and the top-down view during the surgery, which limits the surgeon's ability to estimate depth. To alleviate such difficulty, we propose to automate the tool-navigation task by learning to predict relative goal position on the retinal surface from the current tool-tip position. Given an estimated target on the retina, we generate an optimal trajectory leading to the predicted goal while imposing safety-related physical constraints aimed to minimize tissue damage. As an extended task, we generate goal predictions to various points across the retina to localiz

SUBMITTER: Kim JW 

PROVIDER: S-EPMC8549631 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature