Towards understanding the suture/ligature skills during the training process using WKS-2RII

  • Jorge Solis
  • Nobuki Oshima
  • Hiroyuki Ishii
  • Noriyuki Matsuoka
  • Kazuyuki Hatake
  • Atsuo Takanishi
Original Article



Nowadays, most of the surgical training programs follow a duration-based format that focuses on improving technical skills of trainees for a fixed amount of time before declaring their proficiency. More recently, different approaches have been proposed for the skills assessment; such as the objective structured clinical examination (OSCE). The OSCE consists of different stations in which trainees are required to perform practical exams while their performance is evaluated by examiners. However, their performance cannot be easily assessed by the simple observation of the task. As a result, no standard evaluation criteria can be conceived.


Thanks to the recent advances in Robot Technology (RT); more efficient training systems can be conceived. In particular, authors believe in the importance of developing automated training devices designed to provide training progress quantitative information of trainees. For this reason, at Waseda University, since 2004, we have proposed as a long-term research goal, the development of a Patient Robot which nearly reproduces the human body anatomy and physiology by embedding sensors and actuators into a human model. Due to the complexity of patient robot development, as a first approach, we have proposed the development of a Suture/Ligature Training System. In this paper, the details of Waseda-Kyotokagaku Suture No. 2 Refined II (WKS-2RII) are presented. The WKS-2RII has been designed to reproduce the task conditions of the suture and ligature as well as to provide quantitative information of artificial skin movement, and the physical properties of the suture. From such collected data, we have proposed an Evaluation Function that integrates all the proposed evaluation parameters.


In order to verify the effectiveness of the WKS-2RII, a set of experiments were proposed to analyze the performance of subjects while performing the task with the WKS-2RII. The experiments were designed to determine if the proposed system may provide more detailed information of the task in a quantitative way. From the experimental results, we have confirmed that the WKS-2RII is capable of providing quantitative assessment of the task. In contrast to the conventional training methods (i.e., OSCE, etc.), the WKS-2RII can provide more detailed information of the task performance, so that the proposed system can detect the differences among different level of expertise (five surgeons, five medical students and five unskilled persons) as well as detect improvements of trainees by plotting the learning curve.


In this paper, we have presented the improvements on the WKS-2RII and a unique evaluation function has been proposed. Regarding the weighting coefficients, the discriminant analysis method was used to determine the optimal values of the weighting coefficients.


Medical training system Sensors Suture 


  1. 1.
    Ryan Brydges R, Kurahashi A, Brümmer V, Satterthwaite L, Classen R, Dubrowski A (2008) Developing criteria for proficiency-based training of surgical technical skills using simulation: changes in performances as a function of training year. American College of Surgeons ElsevierGoogle Scholar
  2. 2.
    Xeroulis GJ, Park J, Moulton C-A, Reznick RK, LeBlanc V, Dubrowski A (2007) Teaching suturing and knot-tying skills to medical students: A randomized controlled study comparing computer-based video instruction and (concurrent and summary) expert feedback. Surgery 141: 442–449PubMedCrossRefGoogle Scholar
  3. 3.
    Solis J (2004) Robotic control systems for learning and teaching human skills. Ph.D. dissertation, Perceptual Robotics Laboratory, Scuola Superiore Sant’Anna, Pisa, ItalyGoogle Scholar
  4. 4.
    Solis J, Marcheshi S, Frisoli A, Avizzano CA, Bergamasco M (2007) Reactive robots system: an active human/robot interaction for transferring skills from robot to unskilled persons. Adv Robot J 21(3/4): 267–291CrossRefGoogle Scholar
  5. 5.
    Harden RM, Stevenson M, Downie WW, Wilson GM (1975) Assessment of clinical competence using objective structured clinical examination. Br Med J 1: 447–451PubMedCrossRefGoogle Scholar
  6. 6.
    Oshima N, Aizuddin M, Midorikawa R, Solis J, Ogura Y, Takanishi A (2006) Development of a suture training simulator with quantitative evaluation function. In: Proceedings of the International Special Topic Conference on Information Technology in Biomedicine, ID 104Google Scholar
  7. 7.
    Oshima N, Aizuddin M, Midorikawa R, Solis J, Ogura Y, Takanishi A (2007) Development of a suture/ligature training system designed to provide quantitative information of the learning progress of trainees. In: Proceedings of International Conference on Robotics and Automation, pp 2285–2291Google Scholar
  8. 8.
    Oshima N, Solis J, Ishii H, Matsuoka H, Hatake K, Takanishi A (2007) Acquisition of quantitative data for the detailed analysis of the suture/ligature tasks with the WKS-2R. In: Proceedings of the International Special Topic Conference on Information Technology in Biomedicine, A2–A6Google Scholar
  9. 9.
    Friedman JH (1989) Regularized discriminant analysis. J Am Stat Assoc 84(405): 165–175CrossRefGoogle Scholar
  10. 10.
    Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann ArdorGoogle Scholar
  11. 11.
    Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79: 2554–2558PubMedCrossRefGoogle Scholar
  12. 12.
    Blue AV, Griffith CH, Wilson J (1999) Surgical teaching quality makes a difference. Am J Surg 177(1): 86–89PubMedCrossRefGoogle Scholar
  13. 13.
    Solis J, Suefuji K, Taniguchi K, Takanishi A (2006) Towards an autonomous musical teaching system from the Waseda Flutist Robot to Flutist Beginners. In: Proceedings of IROS: Workshop on Musical Performance Robots and Its Applications, pp 24–27Google Scholar

Copyright information

© CARS 2008

Authors and Affiliations

  • Jorge Solis
    • 1
  • Nobuki Oshima
    • 2
  • Hiroyuki Ishii
    • 3
  • Noriyuki Matsuoka
    • 4
  • Kazuyuki Hatake
    • 4
  • Atsuo Takanishi
    • 1
  1. 1.Department of Modern Mechanical Engineering/Humanoid Robotics InstituteWaseda UniversityTokyoJapan
  2. 2.Graduate School of Advanced Science and EngineeringWaseda UniversityTokyoJapan
  3. 3.Consolidated Research Institute for Advanced Science and Medical CareWaseda UniversityTokyoJapan
  4. 4.Educational Instruments Division/Manufacturing System DepartmentKyotokagaku Co. Ltd.KyotoJapan

Personalised recommendations