Abstract

  Neurosurgery has customarily been at the cutting edge of propelling innovations, adjusting new strategies and gadgets effectively with an end goal to build the security and viability of brain surgery procedures. Among these adjustments is the surgical robot technology. This paper features a portion of the all the more encouraging frameworks in neurosurgical robotics, integrating brain surgical robots being used and being advanced. The reason for this paper is twofold, to address the most encouraging models for neurosurgical applications, and to examine a portion of the entanglements of robotic neurosurgery given the exceptional framework of the brain. The utilization of robotic assistance and input direction on surgical operations could improve the specialization of the experts during the underlying period of the expectation to absorb information.

Keywords: Medical and Surgical Robots; Surgical Applications; Brain Surgery; Minimally Invasive.

References

1. PS Jagadeesh Kumar., et al. “Robotic Simulation of Human Brain Using Convolutional Deep Belief Networks”. International Journal of Intelligent Machines and Robotics 1.2 (2018): 180-191.
2. C Stu., et al. “Robotic technology in spine surgery: current applications and future developments”. Intraoperative Imaging 109 (2011): 241-245.
3. S Badaan and D Stoianovici. “Robotic systems: past, present, and future,” Robotics in Genitourinary Surgery, Springer, New York, NY, USA (2011): 655-665.
4. PS Jagadeesh Kumar., et al. “Deep Robotics and Medical Applications”. Baker and Taylor Publisher Services, North Carolina, United States (2019).
5. Singh I. “Robotics in urological surgery: review of current status and maneuverability, and comparison of robot-assisted and traditional laparoscopy”. Computer Aided Surgery 16.1 (2011): 38-45.
6. GP Moustris., et al. “Evolution of autonomous and semi-autonomous robotic surgical systems: a review of the literature”. International Journal of Medical Robotics and Computer Assisted Surgery 7.4 (2011): 375-392.
7. PS Jagadeesh Kumar., et al. “Deep Robotics in Computational Neuroscience and Human Brain Behavior”. The World Congress on Engineering and Computer Science (WCECS’18) Proceedings of International Conference on Intelligent Automation and Robotics (ICIAR’18) San Francisco, USA (2018): 66-71.
8. B Challacombe and D Stoianovici. “The basic science of robotic surgery”. Urologic Robotic Surgery in Clinical Practice (2009): 1-23.
9. H Kenngott., et al. “Status of robotic assistance: a less traumatic and more accurate minimally invasive surgery?” Langenbeck's Archives of Surgery 397.3 (2012): 1-9.
10. P Gomes. “Surgical robotics: reviewing the past, analysing the present, imagining the future”. Robotics and Computer-Integrated Manufacturing 27.2 (2011): 261-266.
11. L Guo., et al. “A survey on the gastrointestinal capsule micro-robot based on wireless and optoelectronic technology”. Journal of Nanoelectronics and Optoelectronics 7.2 (2012): 123-127.
12. A M Okamura., et al. “Medical and health-care robotics”. IEEE Robotics and Automation Magazine 17.3 (2010): 26-37. 
13. N Fragopanagos and JG Taylor. “Emotion recognition in human-computer interaction”. Neural Networks 18.4 (2005): 389-405.
14. C Chelba., et al. “Retrieval and browsing of spoken content”. IEEE Signal Process. Mag 25.3 (2008): 39-49.
15. F Biadsy. “Automatic dialect and accent recognition and its application to speech recognition,” Ph.D. thesis, Graduate School Arts Sci., Columbia Univ., New York City, NY, USA (2011): 1-171.
16. T Bocklet., et al. “Age and gender recognition for telephone applications based on GMM supervectors and support vector machines”. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process (2008): 1605-1608.
17. MIA Shahin. “Gender-dependent emotion recognition based on HMMs and SPHMMs”. International Journal of Speech Technology 16.2 (2013): 133-141.
18. O Abdel-Hamid., et al. “Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition,” In Proc. IEEE Int. Conf. Acoust., Speech Signal Process (2012): 4277-4280.
19. P Domingos. “A few useful things to know about machine learning”. Commun. ACM 55.10 (2012): 78-87.
20. JT Senders., et al. “An introduction and overview of machine learning in neurosurgical care”. Acta Neurochirurgica 160.1 (2018): 29-38.  
21. LC Resende., et al. “Support vector machine application in composite reliability assessment”. In Proc. 18th Int. Conf. Intell. Syst. Appl. Power Syst. (ISAP), Porto, Portugal (2015).
22. C Fredouille., et al. “Application of Automatic Speaker Recognition techniques to pathological voice assessment (dysphonia)”. In Proc. Eur. Conf. Speech Commun. Technol. (Eurospeech) (2005): 149-152.
23. R Sutton and A Barto. “Reinforcement Learning: An Introduction,” Cambridge, MA, USA: MIT Press, Second Edition (2019).

Citation

Citation: J Ruby., et al. “Integrating Medical Robots for Brain Surgical Applications". Acta Scientific Medical Sciences 4.2 (2020): 169-174.

Copyright

Copyright: © 2020 J Ruby., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.