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 - Funding Information:
Manuscript received March 13, 2019; revised May 15, 2019; accepted June 26, 2019. Date of publication July 1, 2019; date of current version March 19, 2020. This work was supported by the U.S. National Institutes of Health under Grant 1R01EB023943-01. The work of C. He was supported in part by the China Scholarship Council under Grant 201706020074, in part by the National Natural Science Foundation of China under Grant 51875011, and in part by the National Hi-Tech Research and Development Program of China under Grant 2017YFB1302702. The work of P. Gehlbach was supported in part by Research to Prevent Blindness, New York, New York, USA, and gifts by the J. Willard and Alice S. Marriott Foundation, the Gale Trust, Mr. Herb Ehlers, Mr. Bill Wilbur, Mr. and Mrs. Rajandre Shaw, Ms. Helen Nassif, Ms. Mary Ellen Keck, Don and Maggie Feiner, and Mr. Ronald Stiff. (Corresponding author: Changyan He.) C. He is with the School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China, and also with the Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218 USA (e-mail:,changyanhe_mech@buaa.edu.cn).
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
VL - 67
SP - 966
EP - 977
JO - IRE transactions on medical electronics
JF - IRE transactions on medical electronics
SN - 0018-9294
IS - 4
M1 - 8752071
ER -