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International Journal of Computer Assisted Radiology and Surgery

Published in:

01-09-2018 | Original Article

Laparoscopic training using a quantitative assessment and instructional system

Authors: T. Yamaguchi, R. Nakamura

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 9/2018

Abstract

Purpose

Laparoscopic surgery requires complex surgical skills; hence, surgeons require regular training to improve their surgical techniques. The quantitative assessment of a surgeon’s skills and the provision of feedback are important processes for conducting effective training. The aim of this study was to develop an inexpensive training system that provides automatic technique evaluation and feedback.

Methods

We detected the instrument using image processing of commercial web camera images and calculated the motion analysis parameters (MAPs) of the instrument to quantify performance features. Upon receiving the results, we developed a method of evaluating the surgeon’s skill level. The feedback system was developed using MAPs-based radar charts and scores for determining the skill level. These methods were evaluated using the videos of 38 surgeons performing a suturing task.

Results

There were significant differences in MAPs among surgeons; therefore, MAPs can be effectively used to quantify a surgeon’s performance features. The results of skill evaluation and feedback differed greatly between skilled and unskilled surgeons, and it was possible to indicate points of improvement for the procedure performed in this study. Furthermore, the results obtained for certain novice surgeons were similar to those obtained for skilled surgeons.

Conclusion

This system can be used to assess the skill level of surgeons, independent of the years of experience, and provide an understanding of the individual’s current surgical skill level effectively. We conclude that our system is useful as an inexpensive laparoscopic training system that might aid in skill improvement.

Sroka G, Feldman LS, Vassiliou MC, Kaneva PA, Fayez R, Fried GM (2010) Fundamentals of laparoscopic surgery simulator training to proficiency improves laparoscopic performance in the operating room—a randomized controlled trial. Am J Surg 199(1):115–120CrossRefPubMed

Stefanidis D, Hope WW, Korndorffer JR Jr, Markley S, Scott DJ (2010) Initial laparoscopic basic skills training shortens the learning curve of laparoscopic suturing and is cost-effective. J Am Coll Surg 210(4):436–440CrossRefPubMed

Scott DJ, Young WN, Tesfay ST, Frawley ST, Rege ST, Jones ST (2001) Laparoscopic skills training. Am J Surg 182(2):137–142CrossRefPubMed

Ahmed A, Ishman SL, Laeeq K, Bhatti NI (2013) Assessment of improvement of trainee surgical skills in the operating room for tonsillectomy. Laryngoscope 123(7):1639–1644CrossRefPubMed

Uemura M, Tomikawa M, Kumashiro R, Miao T, Souzaki R, Ieiri S, Ohuchida K, Lefor AT, Hashizume M (2014) Analysis of hand motion differentiates expert and novice surgeons. J Surg Res 188(1):8–13CrossRefPubMed

Sugino T, Kawahira H, Nakamura R (2014) Surgical task analysis of simulated laparoscopic cholecystectomy with a navigation system. Int J Comput Assist Radiol Surg 9(5):825–836CrossRefPubMed

Sy Chung, Landsittel D, Chon Ch, Ng Ch, Fuchs Ch (2005) Laparoscopic skills training using a webcam trainer. J Urol 173(1):180–183CrossRef

Beard JH, Akoko L, Mwanga A, Mkony C, O’Sullivan P (2014) Manual laparoscopic skills development using a low-cost trainer box in Tanzania. J Surg Educ 71(1):85–90CrossRefPubMed

Suzuki K, Nakamura R, Sugino T (2014) Surgical instruments trajectory analysis for training of endoscopic surgery. Trans Jpn Soc Med Biol Eng 52(suppl):O-267–O-268

10.

Yamaguchi T, Suzuki K, Nakamura R (2016) Development of a visualization and quantitative assessment system of laparoscopic surgery skill based on trajectory analysis from USB camera image. Int J Comput Assist Radiol Surg 11(suppl):S254–256

11.

Kaehler A, Bradski G (2008) Learning OpenCV. O’reilly Media

12.

Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17(1–3):185–203CrossRef

13.

Brunelli R (2009) Template matching techniques in computer vision: theory and practice. Wiley, HobokenCrossRef

14.

Morita S, Yamazawa K, Terazawa M, Yokoya N (2005) Networked remote surveillance system using omnidirectional image sensors. Ins IEICE Trans Inf Syst 88(5):864–875 (in Japanese)

15.

Ward JH Jr (1963) Hierarchical grouping to optimize an objective function. J Am Stat Assoc 58(301):236–244CrossRef

16.

Krupa A, Gangloff J, Doignon C, de Mathelin MF, Morel G, Leroy J, Soler L, Marescaux J (2003) Autonomous 3-D positioning of surgical instruments in robotized laparoscopic surgery using visual "servoing". IEEE T Robot Autom 9:842–853CrossRef

17.

Tonet O, Ramesh TU, Megali G, Dario P (2006) Tracking endoscopic instruments without localizer: image analysis-based approach. Stud Health Technol Inform 119:544–549PubMed

18.

Speidel S, Delles M, Gutt C, Dillmann R (2006) Tracking of instruments in minimally invasive surgery for surgical skill analysis. Med Imag Augment Real 4091:148–155CrossRef

19.

Voros S, Long J, Cinquin P (2006) Automatic localization of laparoscopic instruments for the visual servoing of an endoscopic camera holder. Med Image Comput Comput Assist Interv 4190:535–542

20.

Bouarfa L, Akman O, Schneider A, Jonker PP, Dankelman J (2011) In vivo real-time tracking of surgical instruments in endoscopic video. Minim Invasive Ther Allied Technol 21:129–134CrossRefPubMed

21.

Climent J, Marés P (2004) Automatic instrument localization in laparoscopic surgery. Electron Lett Comput Vis Image Anal 4:21–31CrossRef

22.

Zia A, Sharma Y, Bettadapura V, Sarin LS, Ploetz T, Clements AM, Essa AI (2016) Automated video-based assessment of surgical skills for training and evaluation in medical schools. Int J Comput Assist Radiol Surg 11(9):1623–1636CrossRefPubMed

23.

Ahmidi N, Tao L, Sefati S, Gao Y, Lea C, Haro BB, Zappella L, Khudanpur S, Vidal R, Hager GD (2017) A dataset and benchmarks for segmentation and recognition of gestures in robotic surgery. IEEE Trans Biomed Eng 64(9):2025–2041CrossRefPubMedPubMedCentral

24.

Oropesa I, Sanchez-Gonzalez P, Chmarra MK, Lamata P, Fernandez A, Sanchez-Margallo JA, Jansen FW, Dankelman J, Sánchez-Margallo FM, Gómez EJ (2013) EVA: laparoscopic instrument tracking based on endoscopic video analysis for psychomotor skills assessment. Surg Endosc 27:1029–1039CrossRefPubMed

25.

Raju A, Wang S, Huang J. (2016) M2CAI surgical tool detection challenge report. http://camma.u-strasbg.fr/m2cai2016/reports/Raju-Tool.pdf

26.

Sahu M, Mukhopadhyay A, Szengel A, Zachow S (2016) Tool and phase recognition using contextual CNN features. arXiv:1610.08854

27.

Hogle NJ, Liu Y, Ogden RT, Fowler DL (2014) Evaluation of surgical fellows’ laparoscopic performance using global operative assessment of laparoscopic skills (GOALS). Surg Endosc 28:1284–1290CrossRefPubMed

28.

Vassiliou MC, Feldman LS, Andrew CG, Bergman S, Leffondre K, Stanbridge D, Fried GM (2005) A global assessment tool for evaluation of intraoperative laparoscopic skills. Am J Surg 190:107–113CrossRefPubMed

29.

Kurashima Y, Feldman LS, Al-Sabah S, Kaneva PA, Fried GM, Vassiliou MC (2011) A tool for training and evaluation of laparoscopic inguinal hernia repair: the global operative assessment of laparoscopic skills-groin hernia (GOALS-GH). Am J Surg 201:54–61CrossRefPubMed

30.

Matsuda T, Kanayama H, Ono Y, Kawauchi A, Mizoguchi H, Nakagawa K, Iwamura M, Shigeta M, Habuchi T, Terachi T (2014) Reliability of laparoscopic skills assessment on video: 8-year results of the endoscopic surgical skill qualification system in Japan. J Endourol 28:1374–1378CrossRefPubMed

31.

Dath D, Regehr G, Birch D, Schlachta C, Poulin E, Mamazza J, Reznick R, Macrae HM (2004) Toward reliable operative assessment: the reliability and feasibility of videotaped assessment of laparoscopic technical skills. Surg Endosc 18:1800–1804CrossRefPubMed

32.

Satava RM, Cuschieri A, Hamdorf J (2003) Metrics for objective assessment. Surg Endosc 17:220–226CrossRefPubMed

33.

Korndorffer JR Jr, Dunne JB, Sierra R, Stefanidis D, Touchard CL, Scott DJ (2005) Simulator training for laparoscopic suturing using performance goals translates to the operating room. J Am Coll Surg 201:23–29CrossRefPubMed

Title: Laparoscopic training using a quantitative assessment and instructional system
Authors: T. Yamaguchi
R. Nakamura
Publication date: 01-09-2018
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 9/2018
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-018-1771-1

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Laparoscopic training using a quantitative assessment and instructional system

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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