Surgical subtask automation — Soft tissue retraction

Robot-assisted surgery is becoming standard-of-care in minimally invasive surgery. Given the intense development in this area, many believe that the next big step is surgical subtask automation, the partial automation of certain elements of the procedure. Autonomous execution at lower task levels has the potential to safely improve one element of a surgical process. Automation by artificial intelligence may significantly improve surgery with better accuracy and targeting, that can shorten the recovering time of the patient. Furthermore, partial automation can also help surgeons efficiently by reducing the fatigue in the case of time-consuming operations. In this paper, we present the automation of soft tissue retraction, an often recurring subtask of surgical interventions. Soft tissue retraction plays an important role in laparoscopic cholecystectomy, e.g., during the exploration of the Calot triangle, automatic retraction would streamline the procedure. The presented method only relies on a stereo camera image feed, and therefore does not put additional overhead on the already crowded operating room. We developed and tested multiple control methods for soft tissue retraction built on each other: a simple proportional control for reference, one using Hidden Markov Models for state estimation, and one employing fuzzy logic. Our method was tested comparatively with all three controllers in a simplified phantom environment.

[1]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[2]  Alex Bateman,et al.  An introduction to hidden Markov models. , 2007, Current protocols in bioinformatics.

[3]  Peter Kazanzides,et al.  An open-source research kit for the da Vinci® Surgical System , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Imre J. Rudas,et al.  Towards surgical subtask automation — Blunt dissection , 2017, 2017 IEEE 21st International Conference on Intelligent Engineering Systems (INES).

[5]  Kenneth Y. Goldberg,et al.  Tumor localization using automated palpation with Gaussian Process Adaptive Sampling , 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

[6]  Jonathan Liss,et al.  Safety and efficacy of laparoscopic cholecystectomy. A prospective analysis of 100 initial patients. , 1991, Annals of surgery.

[7]  Pieter Abbeel,et al.  Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[8]  John Kenneth Salisbury,et al.  The Intuitive/sup TM/ telesurgery system: overview and application , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  Carlo Tomasi.  Camera Calibration , 2002 .

[10]  Kenneth Y. Goldberg,et al.  Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[11]  Imre J. Rudas,et al.  Origins of surgical robotics: From space to the operating room , 2016 .

[12]  Mamoru Mitsuishi,et al.  Trajectory planning under different initial conditions for surgical task automation by learning from demonstration , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).