INTELLIGENT ROBOTIC ASSISTANCE IN DENTAL SURGERY: A REINFORCEMENT LEARNING-BASED MODEL

Mahendra Eknath Pawar

doi:10.29121/shodhkosh.v5.i1.2024.5892

Authors

Dr. Mahendra Eknath Pawar Associate Professor, Vasantdada Patil Pratishthan’s College of Engineering and Visual Arts, India

DOI:

https://doi.org/10.29121/shodhkosh.v5.i1.2024.5892

Keywords:

Dental Robotics, Reinforcement Learning, Surgical Automation, Intelligent Control Systems

Abstract [English]

Robotics in dentoalveolar and oral maxillofacial surgery could allow for improved precision, predictability, and safety in difficult procedures. We introduce an RL-based method for robot intelligent control in dental surgery to enhance intraoperative decision-making and instrument handling. The system enables (semi-)automatic steering of surgical instruments by combining sensor information in real-time with 3D representation of the surgical space. The RL agent is trained in a v-rep virtual environment with reward signals for reduced tissue damage, optimized paths, and time-efficient execution. Experimental validation on synthetic and phantom models indicates the capability of the model to accommodate various anatomical discrepancies and to achieve accurate manipulation, surpassing the conventional rule-based robotic systems. This work demonstrates the feasibility of RL-guided robotic systems in dental surgery and paves the path for real-time clinical integration in the future.

References

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.

S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. Cambridge, MA, USA: MIT Press, 2005.

J. Y. Lee et al., “Robotic-Assisted Dental Surgery: Current Status and Future Prospects,” Journal of Oral and Maxillofacial Surgery, vol. 78, no. 3, pp. 423–430, Mar. 2020.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2012, pp. 1097–1105.

M. K. Habib, M. M. Gadelrab, and N. F. Tolba, “Robotic Surgery in Dentistry: A Review,” International Journal of Dentistry, vol. 2021, Article ID 5567321, 2021.

V. Mnih et al., “Human-level Control through Deep Reinforcement Learning,” Nature, vol. 518, no. 7540, pp. 529–533, Feb. 2015. DOI: https://doi.org/10.1038/nature14236

T. T. Ngo, H. N. Nguyen, and D. Q. Tran, “A Reinforcement Learning Approach for Surgical Robotics,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 2, pp. 1080–1090, Apr. 2020.