Rate of Orientation Change as a New Metric for Robot-Assisted and Open Surgical Skill Evaluation

Surgeons’ technical skill directly impacts patient outcomes. To date, the angular motion of the instruments has been largely overlooked in objective skill evaluation. To fill this gap, we have developed metrics for surgical skill evaluation that are based on the orientation of surgical instruments. We tested our new metrics on two datasets with different conditions: (1) a dataset of experienced robotic surgeons and nonmedical users performing needle-driving on a dry lab model, and (2) a small dataset of suturing movements performed by surgeons training on a porcine model. We evaluated the performance of our new metrics (angular displacement and the rate of orientation change) alongside the performances of classical metrics (task time and path length). We calculated each metric on different segments of the movement. Our results highlighted the importance of segmentation rather than calculating the metrics on the entire movement. Our new metric, the rate of orientation change, showed statistically significant differences between experienced surgeons and nonmedical users / novice surgeons, which were consistent with the classical task time metric. The rate of orientation change captures technical aspects that are taught during surgeons’ training, and together with classical metrics can lead to a more comprehensive discrimination of skills.

[1]  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).

[2]  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.

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

[4]  Jeremy D. Brown,et al.  An Evaluation of Inanimate and Virtual Reality Training for Psychomotor Skill Development in Robot-Assisted Minimally Invasive Surgery , 2020, IEEE Transactions on Medical Robotics and Bionics.

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

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

[7]  Allison M. Okamura,et al.  Robot-Assisted Surgical Training Over Several Days in a Virtual Surgical Environment with Divergent and Convergent Force Fields , 2019, The Hamlyn Symposium on Medical Robotics.

[8]  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.

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

[10]  Henry C. Lin,et al.  JHU-ISI Gesture and Skill Assessment Working Set ( JIGSAWS ) : A Surgical Activity Dataset for Human Motion Modeling , 2014 .

[11]  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.

[12]  Allison M. Okamura,et al.  Training in divergent and convergent force fields during 6-DOF teleoperation with a robot-assisted surgical system , 2017, 2017 IEEE World Haptics Conference (WHC).

[13]  Timothy M. Kowalewski,et al.  Predicting surgical skill from the first N seconds of a task: value over task time using the isogony principle , 2017, International Journal of Computer Assisted Radiology and Surgery.

[14]  Paolo Fiorini,et al.  Surgical gesture recognition with time delay neural network based on kinematic data , 2019, 2019 International Symposium on Medical Robotics (ISMR).

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

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

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

[18]  Nancy J Hogle,et al.  Documenting a learning curve and test-retest reliability of two tasks on a virtual reality training simulator in laparoscopic surgery. , 2007, Journal of surgical education.

[19]  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.

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

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

[22]  Peter I. Corke,et al.  Robotics, Vision and Control - Fundamental Algorithms in MATLAB® , 2011, Springer Tracts in Advanced Robotics.

[23]  Ana Luisa Trejos,et al.  A Sensorized Instrument for Skills Assessment and Training in Minimally Invasive Surgery , 2009 .

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

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

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

[27]  Ilana Nisky,et al.  Expertise, Teleoperation, and Task Constraints Affect the Speed-Curvature-Torsion Power Law in RAMIS , 2018, J. Medical Robotics Res..

[28]  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.

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

[30]  J. Birkmeyer,et al.  Surgical skill and complication rates after bariatric surgery. , 2013, The New England journal of medicine.

[31]  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.

[32]  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.

[33]  P. Good,et al.  Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses , 1995 .

[34]  Katherine J. Kuchenbecker,et al.  Automatically rating trainee skill at a pediatric laparoscopic suturing task , 2017, Surgical Endoscopy.

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

[36]  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.

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

[38]  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).

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

[40]  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.

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

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

[43]  Melina C Vassiliou,et al.  A global assessment tool for evaluation of intraoperative laparoscopic skills. , 2005, American journal of surgery.

[44]  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.

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

[46]  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.

[47]  A. Goh,et al.  Global evaluative assessment of robotic skills: validation of a clinical assessment tool to measure robotic surgical skills. , 2012, The Journal of urology.

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

[49]  Ziheng Wang,et al.  Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery , 2018, International Journal of Computer Assisted Radiology and Surgery.

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

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

[52]  Masaru Ishii,et al.  Objective Assessment of Surgical Technical Skill and Competency in the Operating Room. , 2017, Annual review of biomedical engineering.

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

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

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

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

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

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

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

[60]  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.

[61]  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.

[62]  Anthony Jarc,et al.  Development and Validation of Objective Performance Metrics for Robot‐Assisted Radical Prostatectomy: A Pilot Study , 2018, The Journal of urology.

[63]  Ilana Nisky,et al.  Using Augmentation to Improve the Robustness to Rotation of Deep Learning Segmentation in Robotic-Assisted Surgical Data , 2019, 2019 International Conference on Robotics and Automation (ICRA).