Optimizing Retinal Surgery: Reinforcement Learning for Enhanced Microscope-Assisted Robotics

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 12

Year of Publication : 2024

Authors : Reena S. Rajan, H. Vennila

10.14445/23488379/IJEEE-V11I12P133

How to Cite?

Reena S. Rajan, H. Vennila, "Optimizing Retinal Surgery: Reinforcement Learning for Enhanced Microscope-Assisted Robotics," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 12, pp. 361-374, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I12P133

Abstract:

Recent progress in ophthalmology provides advanced operating rooms with surgical robots and microscopes. Integrating these tools has a significant impact on the field of retinal surgery. Traditional retinal surgeries were often limited by the risk of tremors and challenges in maintaining steady control during complex surgical procedures, leading to a higher risk of complications. This study proposes a Reinforcement Learning (RL) approach to control a robotic arm in retinal microsurgery to enhance precision and reduce the inherent risks of this delicate procedure. The proposed model consists of several key elements, such as a robotic surgery arm, a microscope, and RL agents to control the surgical instrument in real-time according to the visual feedback from the microscope. The RL agent employs a Deep Q-Network (DQN) architecture by interacting with the environment through a sequence of actions and rewards to enhance the movement of the robotic arm. The model utilizes a Convolutional Neural Network (CNN) to extract features from images or frames for accurate state representation. The results demonstrated superior performance with an accuracy of 95%, precision of 97%, recall of 96%, and an F1 score of 96%. The simulation results confirm the high precision control of the robotic arm for minimizing complications in retinal surgeries.

Keywords:

Retina, Robotic retina surgery, Microscope, Reinforcement learning, Robotic arm.

References:

[1] J. Pitcher et al., “Robotic Eye Surgery: Past, Present, and Future,” Journal of Computer Science & Systems Biology, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Raffaele Nuzzi, and Luca Brusasco, “State of the Art of Robotic Surgery Related to Vision: Brain And Eye Applications of Newly Available Devices,” Eye and Brain, vol. 10, pp. 13-24, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[3] C.N. Riviere, and P.S. Jensen, “A Study of Instrument Motion in Retinal Microsurgery,” In Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No. 00CH37143), Chicago, IL, USA, vol. 1, pp. 59-60. 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Raphael Sznitman et al., “Data-Driven Visual Tracking in Retinal Microsurgery,” 15th International Conference Medical Image Computing and Computer-Assisted Intervention, France, pp. 568-575, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Mingchuan Zhou et al., “Towards Robotic Eye Surgery: Marker-Free, Online Hand-Eye Calibration using Optical Coherence Tomography Images,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3944-3951, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Ali Ebrahimi et al., “Stochastic Force-Based Insertion Depth and Tip Position Estimations of Flexible FBG-Equipped Instruments in Robotic Retinal Surgery,” IEEE/ASME Transactions on Mechatronics, vol. 26, no. 3, pp. 1512-1523, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Mingchuan Zhou et al., “Spotlight-Based 3D Instrument Guidance for Autonomous Task in Robot-Assisted Retinal Surgery,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7750-7757, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Matteo Giuseppe Cereda et al., “Clinical Evaluation of an Instrument-Integrated Oct-Based Distance Sensor for Robotic Vitreoretinal Surgery,” Ophthalmology Science, vol. 1, no. 4, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Swastika Chatterjee et al., “Advancements in Robotic Surgery: Innovations, Challenges and Future Prospects,” Journal of Robotic Surgery, vol. 18, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Jeremy Birch et al., “Trocar Localisation for Robot-Assisted Vitreoretinal Surgery,” International Journal of Computer Assisted Radiology and Surgery, vol. 19, no. 2, pp. 191-198, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yinglun Jian et al., “A Parallel Robot with Remote Centre‐of‐Motion for Eye Surgery: Design, Kinematics, Prototype, and Experiments,” The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 20, no. 4, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Ning Wang et al., “Vision-Based Automatic Control of a Surgical Robot for Posterior Segment Ophthalmic Surgery,” IEEE Transactions on Automation Science and Engineering, pp. 1-12, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Forslund Jacobsen Mads et al., “Robot-Assisted Vitreoretinal Surgery Improves Surgical Accuracy Compared with Manual Surgery: A Randomized Trial in a Simulated Setting,” Retina, vol. 40, no. 11, pp. 2091-2098, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Changyan He et al., “Automatic Light Pipe Actuating System for Bimanual Robot-Assisted Retinal Surgery,” IEEE/ASME Transactions on Mechatronics, vol. 25, no. 6, pp. 2846-2857, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Rongxiao Guo et al., “A Robotic Arm Control System with Simultaneous and Sequential Modes Combining Eye-Tracking with Steady-State Visual Evoked Potential in Virtual Reality Environment,” Frontiers in Neurorobotics, vol. 17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ji Woong Kim et al., “Towards Deep Learning Guided Autonomous Eye Surgery Using Microscope and iOCT Images”, arxiv, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Csaba Szepesvári, Algorithms for Reinforcement Learning, Springer Nature, 2022.
[Google Scholar] [Publisher Link]
[18] Manjunath Jogin et al., “Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning,” In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, pp. 2319-2323, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Junhyuk Oh et al., “Discovering Reinforcement Learning Algorithms,” Advances in Neural Information Processing Systems, vol. 33, pp. 1060-1070, 2020.
[Google Scholar] [Publisher Link]
[20] Eric Rohmer, Surya P.N. Singh, and Marc Freese, “V-REP: A Versatile and Scalable Robot Simulation Framework,” 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, pp. 1321-1326, 2013.
[CrossRef] [Google Scholar] [Publisher Link]