Preliminary study of an RNN-based active interventional robotic system (AIRS) in retinal microsurgery.


School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China. [Email]


OBJECTIVE : Retinal microsurgery requires highly dexterous and precise maneuvering of instruments inserted into the eyeball through the sclerotomy port. During such procedures, the sclera can potentially be injured from extreme tool-to-sclera contact force caused by surgeon's unintentional misoperations.
METHODS : We present an active interventional robotic system to prevent such iatrogenic accidents by enabling the robotic system to actively counteract the surgeon's possible unsafe operations in advance of their occurrence. Relying on a novel force sensing tool to measure and collect scleral forces, we construct a recurrent neural network with long short-term memory unit to oversee surgeon's operation and predict possible unsafe scleral forces up to the next 200 ms. We then apply a linear admittance control to actuate the robot to reduce the undesired scleral force. The system is implemented using an existing "steady hand" eye robot platform. The proposed method is evaluated on an artificial eye phantom by performing a "vessel following" mock retinal surgery operation.
RESULTS : Empirical validation over multiple trials indicates that the proposed active interventional robotic system could help to reduce the number of unsafe manipulation events.
CONCLUSIONS : We develop an active interventional robotic system to actively prevent surgeon's unsafe operations in retinal surgery. The result of the evaluation experiments shows that the proposed system can improve the surgeon's performance.


Interventional system,Medical robot,Recurrent neural network,Retinal surgery,

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