Instrument Orientation-Based Metrics for Surgical Skill Evaluation in Robot-Assisted and Open Needle Driving

The technical skill of surgeons directly impacts patient outcomes. Advanced tracking systems enable the development of objective motion-based metrics for skill evaluation, but these metrics are not sufficient to evaluate the performance in complex surgical tasks. In this study, we developed metrics for surgical skill evaluation that are based on the orientation of the surgical instruments. Experienced robotic surgeons and novice users performed teleoperated (using the da Vinci Research Kit) and open needle-driving. Task time and the rate of orientation change successfully distinguished between experienced surgeons and novice users. Path length and the normalized angular displacement allowed for a good separation only in part of the experiment. Our new promising metrics for surgical skill evaluation captured technical aspects that are taught during surgeons' training. They provide complementing evaluation to those of classical metrics. Orientation-based metrics add value to skill assessment and may be an adjunct to classic objective metrics providing more granular discrimination of skills.

[1]  Gregory D. Hager,et al.  Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions. , 2006 .

[2]  Allison M. Okamura,et al.  Teleoperated versus open needle driving: Kinematic analysis of experienced surgeons and novice users , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[3]  A. Darzi,et al.  The use of electromagnetic motion tracking analysis to objectively measure open surgical skill in the laboratory-based model. , 2001, Journal of the American College of Surgeons.

[4]  Alain Berthoz,et al.  Complex unconstrained three-dimensional hand movement and constant equi-affine speed. , 2009, Journal of neurophysiology.

[5]  Lee W. White,et al.  Content and construct validation of a robotic surgery curriculum using an electromagnetic instrument tracker. , 2012, The Journal of urology.

[6]  Gregory D. Hager,et al.  A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery , 2017, IEEE Transactions on Biomedical Engineering.

[7]  Ana Luisa Trejos,et al.  Development of force-based metrics for skills assessment in minimally invasive surgery , 2014, Surgical Endoscopy.

[8]  A. Darzi,et al.  Measurement of Surgical Dexterity Using Motion Analysis of Simple Bench Tasks , 2003, World Journal of Surgery.

[9]  Jiping He,et al.  Control of hand orientation and arm movement during reach and grasp , 2006, Experimental Brain Research.

[10]  Ferdinando A. Mussa-Ivaldi,et al.  Perception of Delayed Stiffness , 2006 .

[11]  Leonid Zhukov,et al.  Measurements of the level of surgical expertise using flight path analysis from da Vinci robotic surgical system. , 2003, Studies in health technology and informatics.

[12]  Gregory D. Hager,et al.  Sparse Hidden Markov Models for Surgical Gesture Classification and Skill Evaluation , 2012, IPCAI.

[13]  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).

[14]  Mukul Mukherjee,et al.  Accuracy and speed trade‐off in robot‐assisted surgery , 2010, The international journal of medical robotics + computer assisted surgery : MRCAS.

[15]  Shahram Payandeh,et al.  Task and Motion Analyses in Endoscopic Surgery , 1996, Dynamic Systems and Control.

[16]  Etienne Burdet,et al.  Assessing suturing techniques using a virtual reality surgical simulator , 2010, Microsurgery.

[17]  A. Darzi,et al.  Qualitative and quantitative analysis of the learning curve of a simulated surgical task on the da Vinci system , 2004, Surgical Endoscopy And Other Interventional Techniques.

[18]  T. Judkins,et al.  Objective evaluation of expert and novice performance during robotic surgical training tasks , 2009, Surgical Endoscopy.

[19]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[20]  Dmitry Oleynikov,et al.  Effect of visual feedback on surgical performance using the da Vinci surgical system. , 2006, Journal of laparoendoscopic & advanced surgical techniques. Part A.

[21]  A. Okamura,et al.  Effects of robotic manipulators on movements of novices and surgeons , 2014, Surgical Endoscopy.

[22]  Henry C. Lin,et al.  Review of methods for objective surgical skill evaluation , 2011, Surgical Endoscopy.

[23]  David Julià,et al.  Surgical skill and complication rates after bariatric surgery. , 2014 .

[24]  M. Kawato,et al.  Formation and control of optimal trajectory in human multijoint arm movement , 1989, Biological Cybernetics.

[25]  T. Hernandez-Boussard,et al.  A comparison of laparoscopic and robotic assisted suturing performance by experts and novices. , 2010, Surgery.

[26]  Alonzo Kelly,et al.  Mobile Robotics: Mathematics, Models, and Methods , 2013 .

[27]  A. Darzi,et al.  Objective assessment of technical skills in surgery , 2003, BMJ : British Medical Journal.

[28]  Guang-Zhong Yang,et al.  Eye-Gaze Driven Surgical Workflow Segmentation , 2007, MICCAI.

[29]  K. A. Ericsson,et al.  Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. , 2004, Academic medicine : journal of the Association of American Medical Colleges.

[30]  Makoto Hashizume,et al.  Objective assessment of laparoscopic suturing skills using a motion-tracking system , 2011, Surgical Endoscopy.

[31]  Ferdinando A. Mussa-Ivaldi,et al.  Perception and Action in Teleoperated Needle Insertion , 2011, IEEE Transactions on Haptics.

[32]  S. Maeso,et al.  Efficacy of the Da Vinci Surgical System in Abdominal Surgery Compared With That of Laparoscopy: A Systematic Review and Meta-Analysis , 2010, Annals of surgery.

[33]  Amod Jog,et al.  Assessing system operation skills in robotic surgery trainees , 2012, The international journal of medical robotics + computer assisted surgery : MRCAS.

[34]  A. Mihailidis,et al.  Design and validation of an assessment tool for open surgical procedures , 2014, Surgical Endoscopy.

[35]  N. Stergiou,et al.  Robotic surgery training and performance , 2005, Surgical Endoscopy And Other Interventional Techniques.

[36]  Christopher R. Wagner,et al.  The Benefit of Force Feedback in Surgery: Examination of Blunt Dissection , 2007, PRESENCE: Teleoperators and Virtual Environments.

[37]  A. V. Van rij,et al.  Cusum as an aid to early assessment of the surgical trainee , 1995, The British journal of surgery.

[38]  A. Darzi,et al.  Training junior operative residents in laparoscopic suturing skills is feasible and efficacious. , 2006, Surgery.

[39]  Raz Leib,et al.  Minimum acceleration with constraints of center of mass: a unified model for arm movements and object manipulation. , 2012, Journal of neurophysiology.

[40]  A. Lanfranco,et al.  Robotic Surgery: A Current Perspective , 2004, Annals of surgery.

[41]  Erlend Fagertun Hofstad,et al.  A study of psychomotor skills in minimally invasive surgery: what differentiates expert and nonexpert performance , 2013, Surgical Endoscopy.

[42]  A. Cuschieri,et al.  Computer-controlled endoscopic performance assessment system , 1998, Surgical Endoscopy.

[43]  A. Okamura.  Haptic feedback in robot-assisted minimally invasive surgery , 2009, Current opinion in urology.

[44]  T. Grantcharov,et al.  Objective assessment of laparoscopic skills using a virtual reality stimulator , 2005, Surgical Endoscopy.

[45]  Jenny Dankelman,et al.  TrEndo, a device for tracking minimally invasive surgical instruments in training setups , 2006 .

[46]  Allison M. Okamura,et al.  Uncontrolled Manifold Analysis of Arm Joint Angle Variability During Robotic Teleoperation and Freehand Movement of Surgeons and Novices , 2014, IEEE Transactions on Biomedical Engineering.

[47]  Filippo Montevecchi,et al.  An experimental study about haptic feedback in robotic surgery: may visual feedback substitute tactile feedback? , 2016, Journal of Robotic Surgery.

[48]  Ilana Nisky,et al.  The effect of force feedback delay on stiffness perception and grip force modulation during tool-mediated interaction with elastic force fields. , 2015, Journal of neurophysiology.

[49]  Vipul Patel,et al.  Fundamentals of robotic surgery: a course of basic robotic surgery skills based upon a 14‐society consensus template of outcomes measures and curriculum development , 2014, The international journal of medical robotics + computer assisted surgery : MRCAS.

[50]  R. Reznick,et al.  Objective structured assessment of technical skill (OSATS) for surgical residents , 1997, The British journal of surgery.

[51]  Marlies P Schijven,et al.  Contemporary virtual reality laparoscopy simulators: quicksand or solid grounds for assessing surgical trainees? , 2010, American journal of surgery.

[52]  A. G. Gallagher,et al.  Construct validation of the ProMIS simulator using a novel laparoscopic suturing task , 2005, Surgical Endoscopy And Other Interventional Techniques.

[53]  Daniel Arthur James,et al.  Towards a wearable device for skill assessment and skill acquisition of a tennis player during the first serve , 2009 .

[54]  A. Moinzadeh,et al.  Face, content, and construct validity of dV-trainer, a novel virtual reality simulator for robotic surgery. , 2009, Urology.

[55]  P. Viviani,et al.  The law relating the kinematic and figural aspects of drawing movements. , 1983, Acta psychologica.

[56]  A. Darzi,et al.  Dexterity enhancement with robotic surgery , 2004, Surgical Endoscopy And Other Interventional Techniques.

[57]  Peter I. Corke.  Robotics, Vision and Control - Fundamental Algorithms In MATLAB® Second, Completely Revised, Extended And Updated Edition, Second Edition , 2017, Springer Tracts in Advanced Robotics.

[58]  Gregory D. Hager,et al.  Surgical gesture classification from video and kinematic data , 2013, Medical Image Anal..

[59]  P. Morasso.  Three dimensional arm trajectories , 1983, Biological Cybernetics.

[60]  R. Reznick,et al.  Teaching and testing technical skills. , 1993, American journal of surgery.

[61]  P. Fitts.  The information capacity of the human motor system in controlling the amplitude of movement. , 1954, Journal of experimental psychology.

[62]  M S Wilson,et al.  MIST VR: a virtual reality trainer for laparoscopic surgery assesses performance. , 1997, Annals of the Royal College of Surgeons of England.

[63]  Marcia Kilchenman O'Malley,et al.  Smoothness of surgical tool tip motion correlates to skill in endovascular tasks , 2016, IEEE Transactions on Human-Machine Systems.

[64]  R. Reznick,et al.  Testing technical skill via an innovative "bench station" examination. , 1997, American journal of surgery.

[65]  C D Smith,et al.  Assessing laparoscopic manipulative skills. , 2001, American journal of surgery.

[66]  Jenny Dankelman,et al.  Retracting and seeking movements during laparoscopic goal-oriented movements. Is the shortest path length optimal? , 2007, Surgical Endoscopy.

[67]  G. Fried,et al.  What are the Training Gaps for Acquiring Laparoscopic Suturing Skills? , 2017, Journal of surgical education.

[68]  Teodor P. Grantcharov,et al.  Psychomotor performance measured in a virtual environment correlates with technical skills in the operating room , 2009, Surgical Endoscopy.

[69]  Ara Darzi,et al.  Evaluating surgical dexterity during corneal suturing. , 2006, Archives of ophthalmology.