Advertisement

WKA-1R Robot assisted quantitative assessment of airway management

  • Yohan Noh
  • Masanao Segawa
  • Akihiro Shimomura
  • Hiroyuki Ishii
  • Jorge Solis
  • Kazuyuki Hatake
  • Atsuo Takanishi
Original Article

Abstract

Object

The emerging field of medical robotics aims tointroduce intelligent tools for physician support. The main challenges for developing efficient medical robotic training systems are simulating real-world conditions of the task and assuring training effectiveness. High anatomic fidelity has been achieved in current systems, but they are limited to provide merely subjective assessments of the training progress. We simulated airway intubation using a unique medical robot and developed objective performance criteria to assess task performance.

Materials and methods

A patient simulation robot was designed to mimic real-world task conditions and provide objective assessments of training progress. The Waseda– Kyotokagaku Airway No. 1R (WKA-1R) includes a human patient model with embedded sensors. An evaluation function was developed for the WKA-1R to quantitatively assess task performance. The evaluation includes performance indices and coefficient weighting. The performance indices were defined based on experiments carried out with medical doctors and from information found in the medical literature. The performance indices are: intubation time, jaw opening, incisor teeth force, cuff pressure, tongue force and tube position. To determine the weighting of coefficients, we used discriminant analysis.

Results

Experiments were carried out with volunteers to determine the effectiveness of the WKA-1R to quantitatively evaluate their performance while performing airway management. We asked subjects from different levels of expertise (from anesthetists to unskilled) to perform the task. From the experimental results, we determined operator effectiveness using the proposed performance indices. We found a significant difference between the experimental groups by evaluating their performances using the proposed evaluation function (P < 0.05).

Conclusions

The WKA-1R robot was designed to quantitatively acquire information on the performances of trainees during intubation procedures. From the experimental results, we could objectively determine operator effectiveness while providing quantitative task assessments.

Keywords

Medical training system Sensors Airway management 

References

  1. 1.
    Satake J (1999) Is it possible to prevent medical accidents? Practice of activity in the nursing care safety measure committee Starting at analysis of near-miss cases. Jpn J Nurs Adm 9(8): 599–602Google Scholar
  2. 2.
    Solis J (2004) Robotic control systems for learning and teaching human skills. PhD Dissertation, Perceptual Robotics Laboratory, Scuola Superiore Sant’Anna, Pisa, ItalyGoogle Scholar
  3. 3.
    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
  4. 4.
    Kawato M, Gomi H (1992) A computational model of four regions of the cerebellum based on feedback error learning. Biol Cybern 69: 95–103CrossRefGoogle Scholar
  5. 5.
    Mayrose J, Kesavadas T, Chugh K, Dhananjay J, Ellis DE (2003) Utilization of virtual reality for endotracheal intubation training. Resuscitation 59(1): 133–138PubMedCrossRefGoogle Scholar
  6. 6.
    Delson N, Koussa N, Tejani N (2003) Measuring 3D force and motion trajectories of a laryngoscope in the operating room. J Clin Eng 28(4): 211–217Google Scholar
  7. 7.
    Rosen J, Hannaford B, Richard CG, Sinanan SM (2001) Markov modeling of minimally invasive surgery based on tool/ tissue interaction and force/torque signatures for evaluation surgical skills. IEEE Trans Biomed Eng 48: 579–591PubMedCrossRefGoogle Scholar
  8. 8.
    Solis J, Avizzano CA, Bergamasco M (2003) Validating a skill transfer system based on Reactive Robots Technology. In: Proceedings of the international symposium on robot and human interactive communication, pp 175–180Google Scholar
  9. 9.
    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
  10. 10.
    Yohan N, Nagahiro K, Ogura Y, Solis J, Hatake K, Takanishi A (2006) Design of airway management training system. In: Proc of the ieee/embs int. special topic conference on information Technology in biomedicine (ITAB), Greece, ID119Google Scholar
  11. 11.
    Weiss M, Schwarz U, Gerber A-Ch (2000) Difficult airway management: comparison of the bullard laryngoscope with the video optical intubation stylet. Can J Anesth 47(3): 280–284PubMedGoogle Scholar
  12. 12.
    O’Connor HM, Mcgraw RC (1997) Clinical skills training: developing objective assessment instruments. Med Educat 31(5): 359–363CrossRefGoogle Scholar
  13. 13.
    American Hear Association (1987) Instructor’s manual for advanced cardiac life support, 2nd edn. AHA, DallasGoogle Scholar
  14. 14.
    Yohan N, Nagahiro K, Ogura Y, Ishii H, Solis J, Hatake K, Takanishi A (2007) Development of Airway Management Training System which embeds array of sensors on a conventional mannequin. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, pp 1296–1301Google Scholar
  15. 15.
    Aoyama K (2005) Tracheal intubation visual manual of clinical basic techniques, 1st edn. Yodosya, Japan, pp 14–15Google Scholar
  16. 16.
    Martin JL, Bucx MJL, van de Vegt MH, Snijders CJ, Stijnen T, Wesselink P (1996) Transverse forces exerted on the maxillary incisors during laryngoscopy. Can J Anaesth 43(7): 665–671CrossRefGoogle Scholar
  17. 17.
    Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis—a brief tutorial. Institute for Signal and Information Processing, Department of Electrical and Computer Engineering, Mississippi State UniversityGoogle Scholar

Copyright information

© CARS 2008

Authors and Affiliations

  • Yohan Noh
    • 1
  • Masanao Segawa
    • 1
  • Akihiro Shimomura
    • 1
  • Hiroyuki Ishii
    • 2
  • Jorge Solis
    • 3
  • Kazuyuki Hatake
    • 4
  • Atsuo Takanishi
    • 3
  1. 1.Graduate School of Advanced Science and EngineeringWaseda UniversityTokyoJapan
  2. 2.Consolidated Research Institute for Advanced Science and Medical CareWaseda UniversityTokyoJapan
  3. 3.Department of Modern Mechanical Engineering / Humanoid Robotics InstituteWaseda UniversityTokyoJapan
  4. 4.Educational Instruments Division / Management DepartmentKyotokagaku Co. Ltd.KyotoJapan

Personalised recommendations