Literature DB >> 12173880

Task decomposition of laparoscopic surgery for objective evaluation of surgical residents' learning curve using hidden Markov model.

Jacob Rosen1, Massimiliano Solazzo, Blake Hannaford, Mika Sinanan.   

Abstract

OBJECTIVE: Evaluation of the laparoscopic surgical skills of surgical residents is usually a subjective process carried out in the operating room by senior surgeons. The two hypotheses of the current study were: (1) haptic information and tool/tissue interactions (types and transitions) performed in laparoscopic surgery are skill-dependent, and (2) statistical models (Hidden Markov Models--HMMs) incorporating these data are capable of objectively evaluating laparoscopic surgical skills.
MATERIALS AND METHODS: Eight subjects (six residents--two first-year (R1), two third-year (R3), and two fifth-year (R5)--and two expert laparoscopic surgeons) performed laparoscopic cholecystectomy on pigs using an instrumented grasper equipped with force/torque (F/T) sensors at the hand/tool interface, and F/T data was synchronized with video of the operative maneuvers. Fourteen types of tool/tissue (T/T) interactions, each associated with unique F/T signatures, were defined from frame-by-frame video analysis. HMMs for each subject and step of the operation were compared to evaluate the statistical distance between expert surgeons and residents with different skill levels.
RESULTS: The statistical distances between HMMs representing expert surgeons and residents were significantly different (alpha < 0.05). Major differences occurred in: (1) F/T magnitudes; (2) type of T/T interactions and transitions between them; and (3) time intervals for each T/T interaction and overall completion time. The greatest difference in performance was between R1 (junior trainee) and R3 (midlevel trainee). Smaller changes were seen as expertise increased beyond the R3 level.
CONCLUSION: HMMs incorporating haptic and visual information provide an objective tool for evaluating surgical skills. Objective evidence for a "learning curve" suggests that surgical residents acquire a major portion of their laparoscopic skill between year 1 and year 3 of training.

Entities:  

Mesh:

Year:  2002        PMID: 12173880     DOI: 10.1002/igs.10026

Source DB:  PubMed          Journal:  Comput Aided Surg        ISSN: 1092-9088


  14 in total

1.  How long do we need teaching in the operating room? The true costs of achieving surgical routine.

Authors:  Thomas Koperna
Journal:  Langenbecks Arch Surg       Date:  2003-10-14       Impact factor: 3.445

Review 2.  Review of methods for objective surgical skill evaluation.

Authors:  Carol E Reiley; Henry C Lin; David D Yuh; Gregory D Hager
Journal:  Surg Endosc       Date:  2010-07-07       Impact factor: 4.584

3.  Idle time: an underdeveloped performance metric for assessing surgical skill.

Authors:  Anne-Lise D D'Angelo; Drew N Rutherford; Rebecca D Ray; Shlomi Laufer; Calvin Kwan; Elaine R Cohen; Andrea Mason; Carla M Pugh
Journal:  Am J Surg       Date:  2015-01-14       Impact factor: 2.565

4.  Haptic feedback can provide an objective assessment of arthroscopic skills.

Authors:  George Chami; James W Ward; Roger Phillips; Kevin P Sherman
Journal:  Clin Orthop Relat Res       Date:  2008-01-23       Impact factor: 4.176

Review 5.  Surgical process modelling: a review.

Authors:  Florent Lalys; Pierre Jannin
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-09-08       Impact factor: 2.924

6.  Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis.

Authors:  Ignacio Oropesa; Patricia Sánchez-Gonzáez; Magdalena K Chmarra; Pablo Lamata; Rodrigo Pérez-Rodríguez; Frank Willem Jansen; Jenny Dankelman; Enrique J Gómez
Journal:  Surg Endosc       Date:  2014-02       Impact factor: 4.584

7.  A study of crowdsourced segment-level surgical skill assessment using pairwise rankings.

Authors:  Anand Malpani; S Swaroop Vedula; Chi Chiung Grace Chen; Gregory D Hager
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-06-30       Impact factor: 2.924

8.  A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery.

Authors:  Narges Ahmidi; Lingling Tao; Shahin Sefati; Yixin Gao; Colin Lea; Benjamin Bejar Haro; Luca Zappella; Sanjeev Khudanpur; Rene Vidal; Gregory D Hager
Journal:  IEEE Trans Biomed Eng       Date:  2017-01-04       Impact factor: 4.538

9.  Assessing system operation skills in robotic surgery trainees.

Authors:  Rajesh Kumar; Amod Jog; Anand Malpani; Balazs Vagvolgyi; David Yuh; Hiep Nguyen; Gregory Hager; Chi Chiung Grace Chen
Journal:  Int J Med Robot       Date:  2011-11-24       Impact factor: 2.547

Review 10.  What is going on in augmented reality simulation in laparoscopic surgery?

Authors:  Sanne M B I Botden; Jack J Jakimowicz
Journal:  Surg Endosc       Date:  2008-09-24       Impact factor: 4.584

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