Toward Safe Retinal Microsurgery: Development and Evaluation of an RNN-Based Active Interventional Control Framework

• Published in: IEEE transactions on biomedical engineering Vol. 67; no. 4; pp. 966 - 977
• Main Authors: He, Changyan, Patel, Niravkumar, Shahbazi, Mahya, Yang, Yang, Gehlbach, Peter, Kobilarov, Marin, Iordachita, Iulian
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-04-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active control; Active damping; Auditory stimuli; Blood vessels; Electrical impedance; Feedback; Force; Injury prevention; Medical robotics; Microsurgery; Neural networks; recurrent neural network; Recurrent neural networks; Retina; retinal surgery; Robot sensing systems; Robotic surgery; Robotics; Robots; safety in microsurgery; Statistical analysis; Surgeons; Surgery; Tremor
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2019.2926060

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.