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Preliminary study of an RNN-based active interventional robotic system (AIRS) in retinal microsurgery

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Retinal microsurgery requires highly dexterous and precise maneuvering of instruments inserted into the eyeball through the sclerotomy port. During such procedures, the sclera can potentially be injured from extreme tool-to-sclera contact force caused by surgeon’s unintentional misoperations.

Methods

We present an active interventional robotic system to prevent such iatrogenic accidents by enabling the robotic system to actively counteract the surgeon’s possible unsafe operations in advance of their occurrence. Relying on a novel force sensing tool to measure and collect scleral forces, we construct a recurrent neural network with long short-term memory unit to oversee surgeon’s operation and predict possible unsafe scleral forces up to the next 200 ms. We then apply a linear admittance control to actuate the robot to reduce the undesired scleral force. The system is implemented using an existing “steady hand” eye robot platform. The proposed method is evaluated on an artificial eye phantom by performing a “vessel following” mock retinal surgery operation.

Results

Empirical validation over multiple trials indicates that the proposed active interventional robotic system could help to reduce the number of unsafe manipulation events.

Conclusions

We develop an active interventional robotic system to actively prevent surgeon’s unsafe operations in retinal surgery. The result of the evaluation experiments shows that the proposed system can improve the surgeon’s performance.

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References

  1. Rahimy E, Wilson J, Tsao T, Schwartz S, Hubschman J (2013) Robot-assisted intraocular surgery: development of the iriss and feasibility studies in an animal model. Eye 27(8):972

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ueta T, Yamaguchi Y, Shirakawa Y, Nakano T, Ideta R, Noda Y, Morita A, Mochizuki R, Sugita N, Mitsuishi M, Tamaki Y (2009) Robot-assisted vitreoretinal surgery: development of a prototype and feasibility studies in an animal model. Ophthalmology 116(8):1538–1543

    Article  PubMed  Google Scholar 

  3. He C, Huang L, Yang Y, Liang Q, Li Y (2018) Research and realization of a master–slave robotic system for retinal vascular bypass surgery. Chin J Mech Eng 31(1):78

    Article  Google Scholar 

  4. MacLachlan RA, Becker BC, Tabarés JC, Podnar GW, Lobes LA Jr, Riviere CN (2012) Micron: an actively stabilized handheld tool for microsurgery. IEEE Trans Robot 28(1):195–212

    Article  PubMed  Google Scholar 

  5. Kummer MP, Abbott JJ, Kratochvil BE, Borer R, Sengul A, Nelson BJ (2010) Octomag: an electromagnetic system for 5-dof wireless micromanipulation. IEEE Trans Robot 26(6):1006–1017

    Article  Google Scholar 

  6. Hubschman J, Bourges J, Choi W, Mozayan A, Tsirbas A, Kim C, Schwartz S (2010) The microhand: a new concept of micro-forceps for ocular robotic surgery. Eye 24(2):364

    Article  PubMed  Google Scholar 

  7. Edwards T, Xue K, Meenink H, Beelen M, Naus G, Simunovic M, Latasiewicz M, Farmery A, de Smet M, MacLaren R (2018) First-in-human study of the safety and viability of intraocular robotic surgery. Nat Biomed Eng 2(9):249

    Article  Google Scholar 

  8. Gijbels A, Smits J, Schoevaerdts L, Willekens K, Vander Poorten EB, Stalmans P, Reynaerts D (2018) In-human robot-assisted retinal vein cannulation, a world first. Ann Biomed Eng 46(10):1676–1685

    Article  PubMed  Google Scholar 

  9. Üneri A, Balicki MA, Handa J, Gehlbach P, Taylor RH, Iordachita I (2010) New steady-hand eye robot with micro-force sensing for vitreoretinal surgery. In: 2010 3rd IEEE RAS and EMBS international conference on biomedical robotics and biomechatronics (BioRob). IEEE, pp 814–819

  10. Iordachita I, Sun Z, Balicki M, Kang JU, Phee SJ, Handa J, Gehlbach P, Taylor R (2009) A sub-millimetric, 0.25 mn resolution fully integrated fiber-optic force-sensing tool for retinal microsurgery. Int J Comput Assist Radiol Surg 4(4):383–390

    Article  PubMed  PubMed Central  Google Scholar 

  11. He X, Handa J, Gehlbach P, Taylor R, Iordachita I (2014) A submillimetric 3-dof force sensing instrument with integrated fiber bragg grating for retinal microsurgery. IEEE Trans Biomed Eng 61(2):522–534

    Article  PubMed  PubMed Central  Google Scholar 

  12. He X, Balicki M, Gehlbach P, Handa J, Taylor R, Iordachita I (2014) A multi-function force sensing instrument for variable admittance robot control in retinal microsurgery. In: 2014 IEEE international conference on robotics and automation (ICRA). IEEE, pp 1411–1418

  13. Cutler N, Balicki M, Finkelstein M, Wang J, Gehlbach P, McGready J, Iordachita I, Taylor R, Handa JT (2013) Auditory force feedback substitution improves surgical precision during simulated ophthalmic surgery. Investig Ophthalmol Vis Sci 54(2):1316–1324

    Article  Google Scholar 

  14. A. Ebrahimi, C. He, M. Roizenblatt, Patel, S. Niravkumar, Sefati, P. Gehlbach, and I. Iordachita (2018) Real-time sclera force feedback for enabling safe robot assisted vitreoretinal surgery. In: 40th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 3650–3655

  15. Othonos A, Kalli K, Pureur D, Mugnier A (2006) Fibre Bragg gratings. In: Venghaus H (ed) Wavelength filters in fibre optics. Springer, Berlin, pp 189–269

    Chapter  Google Scholar 

  16. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  CAS  PubMed  Google Scholar 

  17. Lipton ZC, Kale DC, Elkan C, Wetzel R (2015) Learning to diagnose with lstm recurrent neural networks. arXiv preprint arXiv:1511.03677

  18. Stollenga MF, Byeon W, Liwicki M, Schmidhuber J (2015) Parallel multi-dimensional lSTM, with application to fast biomedical volumetric image segmentation. In: Advances in neural information processing systems, pp 2998–3006

  19. Sundermeyer M, Schlüter R, Ney H (2012) LSTM neural networks for language modeling. In: Thirteenth annual conference of the international speech communication association

  20. Horise Y, He X, Gehlbach P, Taylor R, Iordachita I (2015) FBG-based sensorized light pipe for robotic intraocular illumination facilitates bimanual retinal microsurgery. In: 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC) (2015). IEEE, pp 13–16

  21. Kumar R, Berkelman P, Gupta P, Barnes A, Jensen PS, Whitcomb LL, Taylor RH (2000) Preliminary experiments in cooperative human/robot force control for robot assisted microsurgical manipulation. In: IEEE international conference on robotics and automation, 2000. Proceedings. ICRA’00, vol 1. IEEE, pp 610–617

  22. Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232

    Article  PubMed  Google Scholar 

  23. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  24. Chollet F (2015) “Keras”. https://github.com/keras-team/keras

  25. He C, Ebrahimi A, Roizenblatt M, Patel N, Yang Y, Gehlbach PL, Iordachita I (2018) User behavior evaluation in robot-assisted retinal surgery. In: 2018 27th IEEE international symposium on robot and human interactive communication (RO-MAN). IEEE, pp 174–179

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Correspondence to Changyan He.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the protocol approved by the Johns Hopkins Institutional Review Board.

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Informed consent was obtained from all individual participants included in the study.

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This work was supported by 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, National Natural Science Foundation of China under Grant 51875011, and National Hi-tech Research and Development Program of China with Grant 2017YFB1302702.

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He, C., Patel, N., Ebrahimi, A. et al. Preliminary study of an RNN-based active interventional robotic system (AIRS) in retinal microsurgery. Int J CARS 14, 945–954 (2019). https://doi.org/10.1007/s11548-019-01947-9

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  • DOI: https://doi.org/10.1007/s11548-019-01947-9

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