TY - JOUR
T1 - Toward Safe Retinal Microsurgery
T2 - Development and Evaluation of an RNN-Based Active Interventional Control Framework
AU - He, Changyan
AU - Patel, Niravkumar
AU - Shahbazi, Mahya
AU - Yang, Yang
AU - Gehlbach, Peter
AU - Kobilarov, Marin
AU - Iordachita, Iulian
N1 - Publisher Copyright:
© 1964-2012 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Objective: Robotics-assisted retinal microsurgery provides several benefits including improvement of manipulation precision. The assistance provided to the surgeons by current robotic frameworks is, however, a 'passive' support, e.g., by damping hand tremor. Intelligent assistance and active guidance are, however, lacking in the existing robotic frameworks. In this paper, an active interventional control framework (AICF) has been presented to increase operation safety by actively intervening the operation to avoid exertion of excessive forces to the sclera. Methods: AICF consists of the following four components: first, the steady-hand eye robot as the robotic module; second, a sensorized tool to measure tool-to-sclera forces; third, a recurrent neural network to predict occurrence of undesired events based on a short history of time series of sensor measurements; and finally, a variable admittance controller to command the robot away from the undesired instances. Results: A set of user studies were conducted involving 14 participants (with four surgeons). The users were asked to perform a vessel-following task on an eyeball phantom with the assistance of AICF as well as other two benchmark approaches, i.e., auditory feedback (AF) and real-time force feedback (RF). Statistical analysis shows that AICF results in a significant reduction of proportion of undesired instances to about 2.5%, compared with 38.4% and 26.2% using AF and RF, respectively. Conclusion: AICF can effectively predict excessive-force instances and augment performance of the user to avoid undesired events during robot-assisted microsurgical tasks. Significance: The proposed system may be extended to other fields of microsurgery and may potentially reduce tissue injury.
AB - Objective: Robotics-assisted retinal microsurgery provides several benefits including improvement of manipulation precision. The assistance provided to the surgeons by current robotic frameworks is, however, a 'passive' support, e.g., by damping hand tremor. Intelligent assistance and active guidance are, however, lacking in the existing robotic frameworks. In this paper, an active interventional control framework (AICF) has been presented to increase operation safety by actively intervening the operation to avoid exertion of excessive forces to the sclera. Methods: AICF consists of the following four components: first, the steady-hand eye robot as the robotic module; second, a sensorized tool to measure tool-to-sclera forces; third, a recurrent neural network to predict occurrence of undesired events based on a short history of time series of sensor measurements; and finally, a variable admittance controller to command the robot away from the undesired instances. Results: A set of user studies were conducted involving 14 participants (with four surgeons). The users were asked to perform a vessel-following task on an eyeball phantom with the assistance of AICF as well as other two benchmark approaches, i.e., auditory feedback (AF) and real-time force feedback (RF). Statistical analysis shows that AICF results in a significant reduction of proportion of undesired instances to about 2.5%, compared with 38.4% and 26.2% using AF and RF, respectively. Conclusion: AICF can effectively predict excessive-force instances and augment performance of the user to avoid undesired events during robot-assisted microsurgical tasks. Significance: The proposed system may be extended to other fields of microsurgery and may potentially reduce tissue injury.
KW - Medical robotics
KW - recurrent neural network
KW - retinal surgery
KW - safety in microsurgery
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U2 - 10.1109/TBME.2019.2926060
DO - 10.1109/TBME.2019.2926060
M3 - Article
C2 - 31265381
AN - SCOPUS:85076525045
SN - 0018-9294
VL - 67
SP - 966
EP - 977
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 4
M1 - 8752071
ER -