More Web Proxy on the site http://driver.im/

article

Fuzzy set theory for performance evaluation in a surgical simulator

Authors:

Ima Hajshirmohammadi,

Shahram PayandehAuthors Info & Claims

Presence: Teleoperators and Virtual Environments, Volume 16, Issue 6

Pages 603 - 622

https://doi.org/10.1162/pres.16.6.603

Published: 01 December 2007 Publication History

Abstract

Increasing interest in computer-based surgical simulators as time-and cost-efficient training tools has introduced a new problem: objective evaluation of surgical performance based on scoring metrics provided by surgical simulators. This project employed fuzzy set theory to design a classifier for performance of a subject training on a surgical simulator, using three categories: novice, intermediate, and expert.

The MIST-VR simulator was used in a user study of 26 subjects with three different surgical skill levels: 8 experienced laparoscopic surgeons (experts), 8 surgical assistants (intermediates), and 10 nurses (novices). Subjects were required to perform four trials of a suturing task and a knot-tying task on the simulator. The performance data were then used to train and test two fuzzy classifiers for each task. The fuzzy classifier was able to classify the users of the system. The models presented a highly nonlinear relationship between the inputs (performance metrics) and output (fuzzy score) of the system, which may not be effectively captured with classical classification approaches. Fuzzy classifiers, however, can offer effective tools to handle the complexity and fuzziness of objective evaluation of surgical performances.

References

[1]

Abonyi, J., Nagy, L., & Szeifert, F. (1999). Adaptive fuzzy inference system and its application in modelling and model based control. Chemical Engineering Research and Design, 77(A), 281-290.

[2]

Berg, D., Berkley, J., Weghorst, S., Raugi, G., Turkiyyah, G., Ganter, M., et al. (2002). Issues in validation of a dermatologic surgery simulator. Medicine Meets Virtual Reality 10, 60-65.

[3]

Beringer, D. B. (2002). Applying performance-controlled systems, fuzzy logic, and fly-by-wire controls to general eviation (Tech. Rep No. DOT/FAA/AM-02/7). Washington, DC: Civil Aerospace Medical Institute Publications.

[4]

Chen, J., Yeasin, M., & Sharma, R. (2003). Visual modelling and evaluation of surgical skill. Pattern Analysis and Applications, 6(1), 1-11.

[5]

Cox, E. (1999). The fuzzy systems handbook: A practitioner's guide to building, using and maintaining fuzzy systems (2nd ed.). San Diego, CA: AP Professional.

[6]

Cuschieri, A. (2001). Human reliability assessment in surgery: A new approach for improving surgical performance and clinical outcome. Annals of the Royal College of surgeons of England, 135(7), 551-555.

[7]

Darzi, A. (2001). Assessment of surgical competence. Quality in Health Care, 10, ii64-ii69.

[8]

Doris, P. (2004). Personal communication. Chief Surgeon, Surrey Memorial Hospital, Surrey, British Columbia, Canada.

[9]

Fayek, A. R., & Sun, Z. (2001). A fuzzy expert system for design performance prediction and evaluation, Canadian Journal of Civil Engineering, 28(1), 1-25.

[10]

Fuzzy logic toolbox user guide (2004). Available at http:// www.mathworks.com/access/helpdesk/help/toolbox/ fuzzy/index.html. Retrieved April 10, 2004.

[11]

Gallagher, A. G., McClure, N., McGuigan, J., Crothers, I., & Browning, J. (1999). Virtual reality training in laparoscopic surgery: A preliminary assessment of minimally invasive surgical trainer virtual reality (MIST VR). Endoscopy, 31(4), 310-313.

[12]

Gómez, J., & Dasgupta, D. (2002). Evolving fuzzy classifiers for intrusion detection. Third Annual IEEE Information Assurance Workshop.

[13]

Gorzalczany, M. B., & Gradzki, P. (1999). Computational intelligence in medical decision support--A comparison of two neuro-fuzzy systems. IEEE International Symposium on Industrial Electronics, 1, 408-413.

[14]

Hartog, M. H. den, Babuska, R., Deketh, H. J. R., Grima, M. A., Verhoef, P. N. W., & Verbruggen, H. B. (1997). Knowledge-based fuzzy model for performance prediction of a rockcutting trencher. International Journal of Approximate Reasoning, 16(1), 43-66.

[15]

Huang, J., Payandeh, S., Doris, P., & Hajshirmohammadi, I. (2005). Fuzzy classification: Towards evaluating performance on a surgical simulator. Medicine Meets Virtual Reality, 13, 194-200.

[16]

Jang, J. S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-684.

[17]

Jordan, J. A., Gallagher, A. G., McGuigan, J., & McClure, N. (2001). Virtual reality training leads to faster adaptation to the novel psychomotor restrictions encountered by laparoscopic surgeons. Surgical Endoscopy, 15(10), 1080-1084.

[18]

Kantrowitz, M., Horstkotte, E., & Joslyn, C. (1997). Fuzzy logic and fuzzy expert systems tutorial. Available at http:// www-2.cs.cmu.edu/Groups/Al/html/faqs/ai/fuzzy/ part1/faq.html. Retrieved April 20, 2004.

[19]

Lomax, A. J. (2003). Personal communication. Simon Fraser University, Department of Kinesiology, Burnaby, British Columbia, Canada.

[20]

Mamdani, E. H. (1977). Application of fuzzy logic to approximate reasoning using linguistic systems. Fuzzy Sets and Systems, 26, 1182-1191.

[21]

MIST Suturing Module--User Manual, (2003). Mentice AB, Gothenburg, Sweden.

[22]

MIST User Manual (2002). Mentice AB, Gothenburg, Sweden.

[23]

Moody, L., Baber, C., Arvanitis, T. N., & Elliott, M. (2003). Objective: metrics for the evaluation of simple surgical skills in real and virtual domains. Presence: Teleoperators and Virtual Environments, 12(2), 207-221.

Digital Library

[24]

Nagy, A. (2003). Personal communication. General surgeon, Vancouver General Hospital, Vancouver, British Columbia, Canada.

[25]

Ota, D., Loftin, B., Saito, T., Lea, R., & Keller, J. (1995). Virtual reality in surgical education. Computers in Biology and Medicine, 25(2), 125-127.

[26]

Payandeh, S., Lomax, A. J., Dill, J., MacKenzie, C. L., & Cao, C. G. L. (2002). On defining metrics for assessing laparoscopic surgical skills in a virtual training environment. Medicine Meets Virtual Reality, 10-15.

[27]

Rahbari, R., Leach, B. W., Dillon, J., & DaSilva, C. W. (2002). Adaptive tuning of a Kalman filter using the fuzzy integral for an intelligent navigation system. 2002 IEEE International Symposium on Intelligent Control, 252-257.

[28]

Rosen, J., Hannaford, B., Richards, C., & Sinanan, M. (2001). Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills. IEEE Transactions on Biomedical Engineering, 48(5), 579-591.

[29]

Stylopoulos, N., Cotin, S., Maithel, S. K., Ottensmeyer, M., Jackson, P. G., Bardsley, R. S., et al. (2004). Computer-enhanced laparoscopic training system (CELTS): Bridging the gap. Surgical Endoscopy, 18(5), 782-789.

[30]

Tendick, F., Downes, M., Goktekin, T., Cavusoglu, M., Feygin, D., Wu, X., et al. (2000). A virtual enviromnent test-bed for training laparoscopic surgical skills. Presence: Teleoperators and Virtual Environments, 9(3), 236-255.

Digital Library

[31]

Torkington, J., Smith, S. G., Rees, B. I., & Darzi, A. (2001). Skill transfer from virtual reality to a real laparoscopic task. Surgical Endoscopy, 15(10), 1076-1079.

[32]

Vanegas, L. V., & Labib, A. W. (2005). Fuzzy approaches to evaluation in engineering design. ASME Journal of Mechanical Design, 127.

[33]

Wilson, M. S., Middlebrook, A., Sutton, C., Stone, R., & Me-Cloy, R. F. (1997). MIST VR: A virtual reality trainer for laparoscopic surgery assesses performance. Annals of the Royal College of Surgeons of England, 79(6), 403-404.

Cited By

Enayati NFerrigno GDe Momi E(2018)Skill-based human---robot cooperation in tele-operated path trackingAutonomous Robots10.1007/s10514-017-9675-442:5(997-1009)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10514-017-9675-4
Nieves DFerri CHernández-Orallo JMonserrat C(2017)Low-level Event Detection System for Minimally-Invasive Surgery TrainingProceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction10.1145/3134230.3134241(1-6)Online publication date: 21-Sep-2017
https://dl.acm.org/doi/10.1145/3134230.3134241
Hamam ASaddik AAlja'am J(2014)A Quality of Experience Model for Haptic Virtual EnvironmentsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/254099110:3(1-23)Online publication date: 17-Apr-2014
https://dl.acm.org/doi/10.1145/2540991
Show More Cited By

Index Terms

Fuzzy set theory for performance evaluation in a surgical simulator
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
    2. Simulation support systems
      1. Simulation environments

Recommendations

The development of an arthroscopic surgical simulator with haptic feedback

Interest in improved training methods for minimally invasive surgery (MIS) has led to the development of a number of computer graphics based surgical simulators. These provide a safe environment for training in surgical procedures and offer potential ...
Bone drilling haptic interaction for orthopedic surgical simulator

Drilling procedure is widely used in orthopedic surgery to position reduced fractured bones and prosthetic components. However, successful execution of bone drilling requires a high level of dexterity and experience, because the drilling resistance is ...
Pedicle Screw Insertion Surgical Simulator
Digital Human Modeling. Applications in Health, Safety, Ergonomics, and Risk Management
Abstract
Scoliosis is a sideway spinal deformity. If the curvature is measured to be more than 50 $^{\circ}$ , surgery is required to straighten the spine. Pedicle screw insertion is a procedure that requires the placement of screws from the pedicle into the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Presence: Teleoperators and Virtual Environments

Presence: Teleoperators and Virtual Environments Volume 16, Issue 6

December 2007

114 pages

ISSN:1054-7460

Issue’s Table of Contents

Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 01 December 2007

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Enayati NFerrigno GDe Momi E(2018)Skill-based human---robot cooperation in tele-operated path trackingAutonomous Robots10.1007/s10514-017-9675-442:5(997-1009)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10514-017-9675-4
Nieves DFerri CHernández-Orallo JMonserrat C(2017)Low-level Event Detection System for Minimally-Invasive Surgery TrainingProceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction10.1145/3134230.3134241(1-6)Online publication date: 21-Sep-2017
https://dl.acm.org/doi/10.1145/3134230.3134241
Hamam ASaddik AAlja'am J(2014)A Quality of Experience Model for Haptic Virtual EnvironmentsACM Transactions on Multimedia Computing, Communications, and Applications10.1145/254099110:3(1-23)Online publication date: 17-Apr-2014
https://dl.acm.org/doi/10.1145/2540991
Riojas MFeng CHamilton ARozenblit J(2011)Knowledge elicitation for performance assessment in a computerized surgical training systemApplied Soft Computing10.1016/j.asoc.2011.01.04111:4(3697-3708)Online publication date: 1-Jun-2011
https://dl.acm.org/doi/10.1016/j.asoc.2011.01.041

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents