Retinal microsurgery is one of the most technically demanding surgeries, during which the surgical tool needs to be inserted into the eyeball and is constantly constrained by the sclerotomy port. During the surgery, any unexpected manipulation could cause extreme tool-sclera contact force leading to sclera damage. Although, a robot assistant could reduce hand tremor and improve the tool positioning accuracy, it cannot prevent or alarm the surgeon about the upcoming danger caused by surgeon's misoperations, i.e., applying excessive force on the sclera. In this paper, we present a new method based on a Long Short Term Memory recurrent neural network for predicting the user behavior, i.e., the contact force between the tool and sclera (sclera force) and the insertion depth of the tool from sclera contact point (insertion depth) in real time (40Hz). The predicted force information is provided to the user through auditory feedback to alarm any unexpected sclera force. The user behavior data is collected in a mock retinal surgical operation on a dry eye phantom with Steady Hand Eye Robot and a novel multi-function sensing tool. The Long Short Term Memory recurrent neural network is trained on the collected time series of sclera force and insertion depth. The network can predict the sclera force and insertion depth 100 milliseconds in the future with 95.29% and 96.57% accuracy, respectively, and can help reduce the fraction of unsafe sclera forces from 40.19% to 15.43%.
Keywords: Neural network; robot-assisted retinal surgery; scleral force.