Task-Level vs. Segment-Level Quantitative Metrics for Surgical Skill Assessment

Journal of Surgical Education

Volumn 73, Issue 3, 2016, Pages 482-489

  Vedula, S Swaroop ^a   Malpani, Anand ^a   Ahmidi, Narges ^a   Khudanpur, Sanjeev ^a   Hager, Gregory ^a   Chen, Chi Chiung Grace ^b  

^a Johns Hopkins University   (United States)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

decomposition; objective skill assessment; robotic surgical skills; segment level skill metrics; task level skill metrics

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; ASSESSMENT OF HUMANS; GESTURE; HUMAN; HUMAN EXPERIMENT; INCISION; LOGISTIC REGRESSION ANALYSIS; PRIORITY JOURNAL; ROBOT ASSISTED SURGERY; SEGMENT LEVEL SKILL METRICS; SKILL; SURGEON; SURGICAL TECHNIQUE; SUTURE ANCHOR; SUTURE NEEDLE; TASK LEVEL SKILL METRICS; TASK PERFORMANCE; TIME; ADULT; CLINICAL COMPETENCE; EDUCATION; FEMALE; MALE; PROSPECTIVE STUDY; ROBOTIC SURGICAL PROCEDURE; STANDARDS; SUTURE TECHNIQUE;

ADULT; CLINICAL COMPETENCE; FEMALE; HUMANS; MALE; PROSPECTIVE STUDIES; ROBOTIC SURGICAL PROCEDURES; SUTURE TECHNIQUES; TIME AND MOTION STUDIES;

EID: 84958206682     PISSN: 19317204     EISSN: 18787452     Source Type: Journal    
DOI: 10.1016/j.jsurg.2015.11.009     Document Type: Article

References (31)

1. H.C. Lin Structure in surgical motion 2010 The Johns Hopkins University Baltimore, MD
2. C.E. Reiley, H.C. Lin, D.D. Yuh, and G.D. Hager Review of methods for objective surgical skill evaluation Surg Endosc 25 2 2011 356 366
3. B. Varadarajan Learning and inference algorithms for dynamical system models of dextrous motion 2011 The Johns Hopkins University Baltimore, MD
4. B. Varadarajan, C. Reiley, H. Lin, S. Khudanpur, and G. Hager Data-derived models for segmentation with application to surgical assessment and training G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, C. Taylor, MICCAI 2009, Part I, LNCS 5761 2009 Springer Berlin, Heidelberg 426 434
5. S.D. Bann, M.S. Khan, and A.W. Darzi Measurement of surgical dexterity using motion analysis of simple bench tasks World J Surg 27 4 2003 390 394
6. R. Aggarwal, A. Dosis, F. Bello, and A. Darzi Motion tracking systems for assessment of surgical skill Surg Endosc 21 2 2007 339
7. V. Datta, S. Mackay, M. Mandalia, and A. Darzi The use of electromagnetic motion tracking analysis to objectively measure open surgical skill in the laboratory-based model J Am Coll Surg 193 5 2001 479 485
8. S. Mackay, V. Datta, M. Mandalia, P. Bassett, and A. Darzi Electromagnetic motion analysis in the assessment of surgical skill: relationship between time and movement ANZ J Surg 72 9 2002 632 634
9. C.E. Reiley, and G.D. Hager Task versus subtask surgical skill evaluation of robotic minimally invasive surgery G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, C. Taylor, MICCAI 2009, Part I, LNCS 5761 2009 Springer Berlin Heidelberg 435 442
10. R. Kumar, A. Jog, B. Vagvolgyi, and et al. Objective measures for longitudinal assessment of robotic surgery training J Thorac Cardiovasc Surg 143 3 2012 528 534
11. R. Kumar, A. Jog, A. Malpani, and et al. Assessing system operation skills in robotic surgery trainees Int J Med Robot 8 1 2012 118 124
12. J.A. Martin, G. Regehr, R. Reznick, and et al. Objective structured assessment of technical skill (OSATS) for surgical residents Br J Surg 84 2 1997 273 278
13. A. Freno Selecting Features by Learning Markov Blankets B. Apolloni, R.J. Howlett, L. Jain, Knowledge-based intelligent information and engineering systems 2007 Springer Berlin, Heidelberg 69 76
14. M. Schemper Predictive accuracy and explained variation Stat Med 22 14 2003 2299 2308
15. W. Pan Akaike's information criterion in generalized estimating equations Biometrics 57 1 2001 120 125
16. M. Scutari Learning Bayesian networks with the bnlearn R package J Stat Softw 35 3 2010 1 22
17. W. Sauerbrei, and M. Schumacher1 Bootstrap and cross-validation to assess complexity of data-driven regression models R.W. Brause, E. Hanisch, Medical Data Analysis 2000 Springer Berlin 234 241
18. E.R. DeLong, D.M. DeLong, and D.L. Clarke-Pearson Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach Biometrics 44 3 1988 837 845
19. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ; 2014.
20. I.J. Dooley, and P.D. O'Brien Subjective difficulty of each stage of phacoemulsification cataract surgery performed by basic surgical trainees J Cataract Refract Surg 32 4 2006 604 608
21. S. Bann, M. Khan, V. Datta, and A. Darzi Surgical skill is predicted by the ability to detect errors Am J Surg 189 4 2005 412 415
22. S. Bann, V. Datta, M. Khan, and A. Darzi The surgical error examination is a novel method for objective technical knowledge assessment Am J Surg 185 6 2003 507 511
23. A. Malpani, S.S. Vedula, C.C. Chen, and G.D. Hager A study of crowdsourced segment-level surgical skill assessment using pairwise rankings Int J Comput Assist Radiol Surg 10 9 2015 1435 1447
24. L. Tao, E. Elhamifar, S. Khudanpur, G.D. Hager, and R. Vidal Sparse hidden Markov models for surgical gesture classification and skill evaluation P. Abolmaesumi, L. Joskowicz, N. Navab, P. Jannin, Information Processing in Computer-Assisted Interventions 167 2012 Springer Berlin, Heidelberg 177
25. N. Ahmidi, Y. Gao, B. Béjar, and et al. String motif-based description of tool motion for detecting skill and gestures in robotic surgery K. Mori, I. Sakuma, Y. Sato, C. Barillot, N. Navab, MICCAI 2013, Part I, LNCS 8149 2013 Springer-Verlag Berlin, Heidelberg 26 33
26. L. Zappella, B. Béjar, G. Hager, and R. Vidal Surgical gesture classification from video and kinematic data Med Image Anal 17 7 2013 732 745
27. B.B. Haro, L. Zappella, and R. Vidal Surgical gesture classification from video data N. Ayache, H. Delingette, P. Golland, K. Mori, MICCAI 2012, Part I, LNCS 7510 2012 Springer-Verlag Berlin, Heidelberg 34 41
28. L. Tao, L. Zappella, G.D. Hager, and R. Vidal Surgical gesture segmentation and recognition K. Mori, I. Sakuma, Y. Sato, C. Barillot, N. Navab, MICCAI 2013, Part III, LNCS 8151 2013 Springer Berlin, Heidelberg 339 346
29. C. Chen, L. White, T. Kowalewski, and et al. Crowd-sourced assessment of technical skills: a novel method to evaluate surgical performance J Surg Res 187 1 2014 65 71
30. C.J. Brady, A.C. Villanti, J.L. Pearson, T.R. Kirchner, O.P. Gupta, and C.P. Shah Rapid grading of fundus photographs for diabetic retinopathy using crowdsourcing J Med Internet Res 16 10 2014 e233
31. L. Maier-Hein, S. Mersmann, D. Kondermann, and et al. Can masses of non-experts train highly accurate image classifiers? P. Golland, N. Hata, C. Barillot, J. Hornegger, R. Howe, MICCAI 2014, Part II, LNCS 8674 2014 Springer International Publishing Switzerland 438 445