1932

Abstract

Training skillful and competent surgeons is critical to ensure high quality of care and to minimize disparities in access to effective care. Traditional models to train surgeons are being challenged by rapid advances in technology, an intensified patient-safety culture, and a need for value-driven health systems. Simultaneously, technological developments are enabling capture and analysis of large amounts of complex surgical data. These developments are motivating a “surgical data science” approach to objective computer-aided technical skill evaluation (OCASE-T) for scalable, accurate assessment; individualized feedback; and automated coaching. We define the problem space for OCASE-T and summarize 45 publications representing recent research in this domain. We find that most studies on OCASE-T are simulation based; very few are in the operating room. The algorithms and validation methodologies used for OCASE-T are highly varied; there is no uniform consensus. Future research should emphasize competency assessment in the operating room, validation against patient outcomes, and effectiveness for surgical training.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-bioeng-071516-044435
2017-06-21
2024-04-16
Loading full text...

Full text loading...

/deliver/fulltext/bioeng/19/1/annurev-bioeng-071516-044435.html?itemId=/content/journals/10.1146/annurev-bioeng-071516-044435&mimeType=html&fmt=ahah

Literature Cited

  1. Weiser TG, Haynes AB, Molina G, Lipsitz SR, Esquivel MM. 1.  2016. Size and distribution of the global volume of surgery in 2012. Bull. World Health Organ. 94:F201–9 [Google Scholar]
  2. Weiser TG, Regenbogen SE, Thompson KD, Haynes AB, Lipsitz SR. 2.  2008. An estimation of the global volume of surgery: a modelling strategy based on available data. Lancet 372:139–44 [Google Scholar]
  3. Birkmeyer JD, Finks JF, O'Reilly A, Oerline M, Carlin AM. 3.  2013. Surgical skill and complication rates after bariatric surgery. N. Engl. J. Med. 369:1434–42 [Google Scholar]
  4. Nathan M, Karamichalis JM, Liu H, del Nido P, Pigula R. 4.  2011. Intraoperative adverse events can be compensated by technical performance in neonates and infants after cardiac surgery: a prospective study. J. Thorac. Cardiovasc. Surg. 142:1098–107 [Google Scholar]
  5. Nathan M, Karamichalis JM, Liu H, Emani S, Baird C. 5.  2012. Surgical technical performance scores are predictors of late mortality and unplanned reinterventions in infants after cardiac surgery. J. Thorac. Cardiovasc. Surg. 144:1095–101 [Google Scholar]
  6. Shuhaiber J, Gauvreau K, Thiagarajan R, Bacha E, Mayer J. 6.  2012. Congenital heart surgeons technical proficiency affects neonatal hospital survival. J. Thorac. Cardiovasc. Surg. 144:1119–24 [Google Scholar]
  7. Parsa CJ, Organ CH Jr., Barkan H. 7.  2000. Changing patterns of resident operative experience from 1990 to 1997. Arch. Surg. 135:570–75 [Google Scholar]
  8. Smith R. 8.  1998. All changed, changed utterly. BMJ 316:1917–18 [Google Scholar]
  9. Szasz P, Louridas M, Harris KA, Aggarwal R, Grantcharov TP. 9.  2015. Assessing technical competence in surgical trainees: a systematic review. Ann. Surg. 261:1046–55 [Google Scholar]
  10. Bhatti NI, Cummings CW. 10.  2007. Viewpoint: competency in surgical residency training: defining and raising the bar. Acad. Med. 82:569–73 [Google Scholar]
  11. Satava RM, Gallagher AG, Pellegrini CA. 11.  2003. Surgical competence and surgical proficiency: definitions, taxonomy, and metrics. J. Am. Coll. Surg. 196:933–37 [Google Scholar]
  12. Sharma B, Mishra A, Aggarwal R, Grantcharov TP. 12.  2011. Non-technical skills assessment in surgery. Surg. Oncol. 20:169–77 [Google Scholar]
  13. Yule S, Flin R, Paterson-Brown S, Maran N. 13.  2006. Non-technical skills for surgeons in the operating room: a review of the literature. Surgery 139:140–49 [Google Scholar]
  14. Reznick RK. 14.  1993. Teaching and testing technical skills. Am. J. Surg. 165:358–61 [Google Scholar]
  15. Pradarelli JC, Campbell DA Jr., Dimick JB. 15.  2015. Hospital credentialing and privileging of surgeons: a potential safety blind spot. JAMA 313:1313–14 [Google Scholar]
  16. Roman H, Marpeau L, Hulsey TC. 16.  2008. Surgeons experience and interaction effect in randomized controlled trials regarding new surgical procedures. Am. J. Obstet. Gynecol. 199:108 [Google Scholar]
  17. Regenbogen SE, Greenberg CC, Studdert DM, Lipsitz SR, Zinner MJ, Gawande AA. 17.  2007. Patterns of technical error among surgical malpractice claims: an analysis of strategies to prevent injury to surgical patients. Ann. Surg. 246:705–11 [Google Scholar]
  18. Hull L, Arora S, Aggarwal R, Darzi A, Vincent C, Sevdalis N. 18.  2012. The impact of nontechnical skills on technical performance in surgery: a systematic review. J. Am. Coll. Surg. 214:214–30 [Google Scholar]
  19. Mishra A, Catchpole K, Dale T, McCulloch P. 19.  2007. The influence of non-technical performance on technical outcome in laparoscopic cholecystectomy. Surg. Endosc. 22:68–73 [Google Scholar]
  20. Martin JA, Regehr G, Reznick R, Macrae H, Murnaghan J. 20.  1997. Objective structured assessment of technical skill (OSATS) for surgical residents. Br. J. Surg. 84:273–78 [Google Scholar]
  21. Ghaderi I, Manji F, Park YS, Juul D, Ott M. 21.  2015. Technical skills assessment toolbox: a review using the unitary framework of validity. Ann. Surg. 261:251–62 [Google Scholar]
  22. Ahmed K, Miskovic D, Darzi A, Athanasiou T, Hanna GB. 22.  2011. Observational tools for assessment of procedural skills: a systematic review. Am. J. Surg. 202469–80.e6
  23. Jelovsek JE, Kow N, Diwadkar GB. 23.  2013. Tools for the direct observation and assessment of psychomotor skills in medical trainees: a systematic review. Med. Educ. 47:650–73 [Google Scholar]
  24. Middleton RM, Baldwin MJ, Akhtar K, Alvand A, Rees JL. 24.  2016. Which global rating scale?. J. Bone Joint Surg. Am. 9875–81
  25. Anderson DD, Long S, Thomas GW, Putnam MD, Bechtold JE, Karam MD. 25.  2015. Objective Structured Assessments of Technical Skills (OSATS) does not assess the quality of the surgical result effectively. Clin. Orthop. Relat. Res. 474:874–81 [Google Scholar]
  26. Faulkner H, Regehr G, Martin J, Reznick R. 26.  1996. Validation of an objective structured assessment of technical skill for surgical residents. Acad. Med. 71:1363–65 [Google Scholar]
  27. Rooney DM, Hungness ES, DaRosa DA, Pugh CM. 27.  2012. Can skills coaches be used to assess resident performance in the skills laboratory?. Surgery 151796–802
  28. Lendvay TS, White L, Kowalewski T. 28.  2015. Crowdsourcing to assess surgical skill. JAMA Surg. 150:1086–87 [Google Scholar]
  29. Ghani KR, Miller DC, Linsell S, Brachulis A, Lane B. 29.  2016. Measuring to improve: peer and crowd-sourced assessments of technical skill with robot-assisted radical prostatectomy. Eur. Urol. 69:547–50 [Google Scholar]
  30. Powers MK, Boonjindasup A, Pinsky M, Dorsey P, Maddox M. 30.  2015. Crowdsourcing assessment of surgeon dissection of renal artery and vein during robotic partial nephrectomy: a novel approach for quantitative assessment of surgical performance. J. Endourol. 30:447–52 [Google Scholar]
  31. Rutegård M, Lagergren J, Rouvelas I, Lagergren P. 31.  2009. Surgeon volume is a poor proxy for skill in esophageal cancer surgery. Ann. Surg. 249:256–61 [Google Scholar]
  32. Sheikh F, Gray RJ, Ferrara J, Foster K, Chapital A. 32.  2010. Disparity between actual case volume and the perceptions of case volume needed to train competent general surgeons. J. Surg. Ed. 67:371–75 [Google Scholar]
  33. Snyder RA, Tarpley MJ, Tarpley JL, Davidson M, Brophy C, Dattilo JB. 33.  Teaching in the operating room: results of a national survey. J. Surg. Ed. 69643–49
  34. Feldman LS, Hagarty SE, Ghitulescu G, Stanbridge D, Fried GM. 34.  2004. Relationship between objective assessment of technical skills and subjective in-training evaluations in surgical residents. J. Am. Coll. Surg. 198:105–10 [Google Scholar]
  35. Porter ME. 35.  2010. What is value in health care?. N. Engl. J. Med. 3632477–81
  36. Bell RH Jr. 36.  2009. Why Johnny cannot operate. Surgery 146:533–42 [Google Scholar]
  37. Chikwe J, de Souza AC, Pepper JR. 37.  2004. No time to train the surgeons. BMJ 328:418–19 [Google Scholar]
  38. Vedula SS, Ishii M, Hager GD. 38.  2016. Perspectives on surgical data science Presented at Worksh. Surg. Data Sci., June 20, Heidelberg, Ger.
  39. Bishop CM. 39.  2009. Pattern Recognition and Machine Learning New York: Springer
  40. Hastie T, Tibshirani R, Friedman J. 40.  2001. The Elements of Statistical Learning New York: Springer
  41. Steyerberg EW. 41.  Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating New York: Springer
  42. Reiley CE, Lin HC, Yuh DD, Hager GD. 42.  2010. Review of methods for objective surgical skill evaluation. Surg. Endosc. 25:356–66 [Google Scholar]
  43. van Hove PD, Tuijthof GJM, Verdaasdonk EGG, Stassen LPS, Dankelman J. 43.  2010. Objective assessment of technical surgical skills. Br. J. Surg. 97:972–87 [Google Scholar]
  44. Oropesa P, Sánchez-González P, Lamata P, Chmarra MK, Pagador JB. 44.  2011. Methods and tools for objective assessment of psychomotor skills in laparoscopic surgery. J. Surg. Res. 171:e81–95 [Google Scholar]
  45. Dosis A, Bello F, Rockall T, Munz Y, Moorthy K. 45.  2003. ROVIMAS: a software package for assessing surgical skills using the da Vinci telemanipulator system. In Proceedings of the 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine326–29 Piscataway, NJ: IEEE
  46. Aizuddin M, Oshima N, Midorikawa R, Takanishi A. 46.  2006. Development of sensor system for effective evaluation of surgical skill. Proceedings of the 1st IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics678–83 Piscataway, NJ: IEEE
  47. Rosen J. 47.  1999. Surgeon-tool force/torque signatures—evaluation of surgical skills in minimally invasive surgery. Medicine Meets Virtual Reality. Convergence of Physical and Informational Technologies: Options for a New Era in Healthcare JD Westwood, HM Hoffman, R Robb, D Stredney 290–96 Lansdale, PA: IOS [Google Scholar]
  48. Hattori M, Egi H, Tokunaga M, Suzuki T, Ohdan H, Kawahara T. 48.  2012. The integrated deviation in the HUESAD (Hiroshima University endoscopic surgical assessment device) represents the surgeon's visual–spatial ability. Proceedings of the 2012 ICME International Conference on Complex Medical Engineering316–20 Piscataway, NJ: IEEE
  49. Wang C, Noh Y, Ishii H, Kikuta G, Ebihara K. 49.  2011. Development of a 3D simulation which can provide better understanding of trainees performance of the task using airway management training system WKA-1RII. In Proceedings of the 2011 IEEE International Conference on Robotics and Biomimetics2635–40 Piscataway, NJ: IEEE
  50. Ahmidi N, Tao L, Sefati S, Gao Y, Lea C. 50.  2017. A dataset and benchmark for segmentation and recognition of gestures in robotic surgery. IEEE Trans. Biomed. Eng. In press. https://doi.10.1109/TBME.2016.2647680
  51. Lea C, Hager GD, Vidal R. 51.  2015. An improved model for segmentation and recognition of fine-grained activities with application to surgical training tasks. IEEE Winter Conf. Appl. Comput. Vis. 2015:1123–29 [Google Scholar]
  52. Gao Y, Vedula SS, Lee GI, Lee MR, Khudanpur S, Hager GD. 52.  2016. Int. J. Comput. Assist. Radiol. Surg. 11987–96
  53. Lalys F, Jannin P. 53.  2014. Surgical process modelling: a review. Int. J. CARS 9:495–511 [Google Scholar]
  54. Ahmidi N, Poddar P, Jones JD, Vedula SS, Ishii L. 54.  2015. Automated objective surgical skill assessment in the operating room from unstructured tool motion in septoplasty. Int. J. CARS 10:981–91 [Google Scholar]
  55. Richstone L, Schwartz MJ, Seideman C, Cadeddu J, Marshall S, Kavoussi LR. 55.  2010. Eye metrics as an objective assessment of surgical skill. Ann. Surg. 252:177–82 [Google Scholar]
  56. Ahmidi N, Hager GD, Ishii L, Fichtinger G, Gallia GL, Ishii M. 56.  2010. Surgical task and skill classification from eye tracking and tool motion in minimally invasive surgery. Med. Image Comput. Comput. Assist. Interv. 13:295–302 [Google Scholar]
  57. Ahmidi N, Hager GD, Ishii L, Gallia GL, Ishii M. 57.  2012. Robotic path planning for surgeon skill evaluation in minimally-invasive sinus surgery. Med. Image Comput. Comput. Assist. Interv. 15:471–78 [Google Scholar]
  58. Ahmidi N, Ishii M, Fichtinger G, Gallia GL, Hager GD. 58.  2012. An objective and automated method for assessing surgical skill in endoscopic sinus surgery using eye-tracking and tool-motion data. Int. Forum Allergy Rhinol. 2:507–15 [Google Scholar]
  59. Ahmidi N, Hager GD, Ishii M. 59.  2012. Towards surgical skill assessment based on fractal patterns in minimally-invasive surgeries. Int. J. CARS 7:185–200 [Google Scholar]
  60. Rosen J, Solazzo M, Hannaford B, Sinanan M. 60.  2001. Objective laparoscopic skills assessments of surgical residents using hidden Markov models based on haptic information and tool/tissue interactions. Stud. Health Technol. Inform. 81:417–23 [Google Scholar]
  61. Rosen J, Hannaford B, Richards CG, Sinanan MN. 61.  2001. Markov modeling of minimally invasive surgery based on tool/tissue interaction and force/torque signatures for evaluating surgical skills. IEEE Trans. Biomed. Eng. 48:579–91 [Google Scholar]
  62. Rosen J, Brown JD, Chang L, Sinanan MN, Hannaford B. 62.  2006. Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model. IEEE Trans. Biomed. Eng. 53:399–413 [Google Scholar]
  63. Chen J, Yeasin M, Sharma R. 63.  2003. Visual modelling and evaluation of surgical skill. Pattern Anal. Appl. 6:1–11 [Google Scholar]
  64. Sharma Y, Bettadapura V, Plötz T, Hammerld N, Mellor S. 64.  2014. Video based assessment of OSATS using sequential motion textures. In Proceedings of the 5th Workshop on Modeling and Monitoring of Computer Assisted Interventions https://smartech.gatech.edu/handle/1853/53651
  65. Sharma Y, Plötz T, Hammerld N, Mellor S, McNaney R. 65.  2014. Automated surgical OSATS prediction from videos. In Proceedings of the 11th IEEE International Symposium on Biomedical Imaging461–64 Piscataway, NJ: IEEE
  66. Khan A, Mellor S, Berlin E, Thompson R, McNaney R. 66.  2015. Beyond activity recognition: skill assessment from accelerometer data. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing1155–66 New York: ACM
  67. Zia A, Sharma Y, Bettadapura V, Sarin EL, Clements MA, Essa I. 67.  2015. Automated assessment of surgical skills using frequency analysis. Med. Image Comput. Comput. Assist. Interv. 11:430–38 [Google Scholar]
  68. Speidel S, Zentek T, Sudra G, Gehrig T, Müller-Stich BP. 68.  2009. Recognition of surgical skills using hidden Markov models. Proc. SPIE 7261:25 [Google Scholar]
  69. Zhang Q, Li B. 69.  2011. Video-based motion expertise analysis in simulation-based surgical training using hierarchical Dirichlet process hidden Markov model. Proceedings of the 2011 International ACM Workshop on Medical Multimedia Analysis and Retrieval19–24 New York: ACM
  70. Horeman T, Rodrigues SP, Jansen FW, Dankelman J, van den Dobbelsteen JJ. 70.  2012. Force parameters for skills assessment in laparoscopy. IEEE Trans. Haptics 5:312–22 [Google Scholar]
  71. Horeman T, Dankelman J, Jansen FW, van den Dobbelsteen JJ. 71.  2014. Assessment of laparoscopic skills based on force and motion parameters. IEEE Trans. Biomed. Eng. 61:805–13 [Google Scholar]
  72. Kowalewski TM. 72.  2012. Real-time quantitative assessment of surgical skill PhD thesis, Univ. Wash., Seattle
  73. Lin Z, Uemura M, Zecca M, Sessa S, Ishii H. 73.  2013. Objective skill evaluation for laparoscopic training based on motion analysis. IEEE Trans. Biomed. Eng. 60:977–85 [Google Scholar]
  74. Zhang Q, Li B. 74.  2015. Relative hidden Markov models for video-based evaluation of motion skills in surgical training. IEEE Trans. Pattern Anal. Mach. Intell. 37:1206–18 [Google Scholar]
  75. Islam G, Kahol K, Li B, Smith M, Patel VL. 75.  2016. Affordable, web-based surgical skill training and evaluation tool. J. Biomed. Inform. 59:102–14 [Google Scholar]
  76. Reiley CE, Hager GD. 76.  2009. Task versus subtask surgical skill evaluation of robotic minimally invasive surgery. Med. Image Comput. Comput. Assist. Interv. 12:435–42 [Google Scholar]
  77. Ahmidi N, Gao Y, Bjar B, Vedula SS, Khudanpur S. 77.  2013. String motif–based description of tool motion for detecting skill and gestures in robotic surgery. Med. Image Comput. Comput. Assist. Interv. 16:26–33 [Google Scholar]
  78. Kumar R, Jog A, Malpani A, Vagvolgyi B, Yuh D. 78.  2012. Assessing system operation skills in robotic surgery trainees. Int. J. Med. Robot. 8:118–24 [Google Scholar]
  79. Kumar R, Jog A, Vagvolgyi B, Nguyen H, Hager G. 79.  2012. Objective measures for longitudinal assessment of robotic surgery training. J. Thorac. Cardiovasc. Surg. 143:528–34 [Google Scholar]
  80. Tao L, Elhamifar E, Khudanpur S, Hager GD, Vidal R. 80.  2012. Sparse hidden Markov models for surgical gesture classification and skill evaluation. Lecture Notes in Computer Science 7330 Information Processing in Computer-Assisted Interventions167–77 New York: Springer
  81. Malpani A, Vedula SS, Chen CCG, Hager GD. 81.  2014. Pairwise comparison-based objective score for automated skill assessment of segments in a surgical task. Lecture Notes in Computer Science 8498 Information Processing in Computer-Assisted Interventions138–47 New York: Springer
  82. Malpani A, Vedula SS, Chen CCG, Hager GD. 82.  2015. A study of crowdsourced segment-level surgical skill assessment using pairwise rankings. Int. J. CARS 10:1435–47 [Google Scholar]
  83. Gomez ED, Aggarwal R, McMahan W, Bark K, Kuchenbecker KJ. 83.  2015. Objective assessment of robotic surgical skill using instrument contact vibrations. Surg. Endosc. 30:1419–31 [Google Scholar]
  84. Fard MJ. 84.  2016. Computational modeling approaches for task analysis in robotic-assisted surgery PhD thesis, pap. 1449, Wayne State Univ., Detroit, Mich.
  85. Vedula SS, Malpani A, Ahmidi N, Khudanpur S, Hager G, Chen CCG. 85.  2016. Task-level versus segment-level quantitative metrics for surgical skill assessment. J. Surg. Ed. 73:482–89 [Google Scholar]
  86. Gomez ED, Aggarwal R, McMahan W, Bark K, Kuchenbecker KJ. 86.  2016. Objective assessment of robotic surgical skill using instrument contact vibrations. Surg. Endosc. 30:1419–31 [Google Scholar]
  87. Rafii-Tari H, Payne CJ, Liu J, Riga C, Bicknell C, Yang GZ. 87.  2015. Towards automated surgical skill evaluation of endovascular catheterization tasks based on force and motion signatures. Proceedings of the IEEE International Conference on Robotics and Automation1789–94 Piscataway, NJ: IEEE
  88. Kramer BD, Losey DP, O'Malley MK. 88.  2016. SOM and LVQ classification of endovascular surgeons using motion-based metrics. Advances in Intelligent Systems and Computing, vol. 428: Advances in Self-Organizing Maps and Learning Vector Quantification227–37 New York: Springer [Google Scholar]
  89. Sewell C. 89.  2007. Automatic performance evaluation in surgical simulation PhD thesis, Stanford Univ., Stanford, CA
  90. Rhienmora P, Haddawy P, Suebnukarn S, Dailey MN. 90.  2009. Providing objective feedback on skill assessment in a dental surgical training simulator. In Lecture Notes in Computer ScienceArtificial Intelligence in Medicine C Combi, Y Shahar, A Abu-Hanna 305–14 New York: Springer [Google Scholar]
  91. Rhienmora P, Haddawy P, Suebnukarn S, Dailey MN. 91.  2011. Intelligent dental training simulator with objective skill assessment and feedback. Artif. Intell. Med. 52:115–21 [Google Scholar]
  92. Liang H, Shi MY. 92.  2010. Surgical skill evaluation model for virtual surgical training. Appl. Mech. Mater. 4041:812–19 [Google Scholar]
  93. Hajshirmohammadi I. 93.  2006. Using fuzzy set theory to objectively evaluate performance on minimally invasive surgical simulators PhD thesis, Simon Fraser Univ., Burnaby, Can.
  94. Leong JJH, Nicolaou M, Atallah L, Mylonas GP, Darzi AW, Yang GZ. 94.  2006. HMM assessment of quality of movement trajectory in laparoscopic surgery. Med. Image Comput. Comput. Assist. Interv. 9:752–59 [Google Scholar]
  95. Megali G, Sinigaglia S, Tonet O, Dario P. 95.  2006. Modelling and evaluation of surgical performance using hidden Markov models. IEEE Trans. Biomed. Eng. 53:1911–19 [Google Scholar]
  96. Loukas C, Georgiou E. 96.  2011. Multivariate autoregressive modeling of hand kinematics for laparoscopic skills assessment of surgical trainees. IEEE Trans. Biomed. Eng. 58:3289–97 [Google Scholar]
  97. Jog A, Itkowitz B, Liu M, DiMaio S, Hager G. 97.  2011. Towards integrating task information in skills assessment for dexterous tasks in surgery and simulation. Proceedings of the 2011 IEEE International Conference on Robotics and Automation5273–78 Piscataway, NJ: IEEE
  98. Arora S, Sevdalis N, Nestel D, Woloshynowych M, Darzi A, Kneebone R. 98.  2010. The impact of stress on surgical performance: a systematic review of the literature. Surgery 147:318–30 [Google Scholar]
  99. Lee G, Lee T, Dexter D, Klein R, Park A. 99.  2007. Methodological infrastructure in surgical ergonomics: a review of tasks, models, and measurement systems. Surg. Innov. 14:153–67 [Google Scholar]
  100. Ericsson KA, Charness N, Feltovich PJ, Hoffman RR. 100.  2006. The Cambridge Handbook of Expertise and Expert Performance New York: Cambridge Univ. Press
  101. Crochet P, Aggarwal R, Dubb SS, Ziprin P, Rajaretnam N. 101.  2011. Deliberate practice on a virtual reality laparoscopic simulator enhances the quality of surgical technical skills. Ann. Surg. 253:1216–22 [Google Scholar]
  102. Bonrath EM, Dedy NJ, Gordon LE, Grantcharov TP. 102.  2015. Comprehensive surgical coaching enhances surgical skill in the operating room: a randomized controlled trial. Ann. Surg. 262:205–12 [Google Scholar]
  103. Messick S. 103.  1995. Validity of psychological assessment: validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. Am. Psychol. 50:741–49 [Google Scholar]
  104. Anderson F, Birch DW, Boulanger P, Bischof WF. 104.  2012. Sensor fusion for laparoscopic surgery skill acquisition. Comput. Aided Surg. 17:269–83 [Google Scholar]
  105. Snaineh STA, Seales B. 105.  2015. Minimally invasive surgery skills assessment using multiple synchronized sensors. Proceedings of the IEEE International Symposium on Signal Processing and Information Technology314–19 Piscataway, NJ: IEEE
  106. Sonnadara R, Rittenhouse N, Khan A, Mihailidis A, Drozdzal G. 106.  2012. A novel multimodal platform for assessing surgical technical skills. Am. J. Surg. 203:32–36 [Google Scholar]
  107. Ruda K, Beekman D, White LW, Lendvay TS, Kowalewski TM. 107.  2013. SurgTrak—a universal platform for quantitative surgical data capture. J. Med. Devices 7:030923 [Google Scholar]
  108. Ostler D, Kranzfelder M, Stauder R, Wilhelm D, Feussner H, Schneider A. 108.  2015. A centralized data acquisition framework for operating theatres. Proceedings of the 17th International Conference on E-Health Networking, Application Services1–5 Piscataway, NJ: IEEE
  109. Zhao X. 109.  2005. Acquisition, storage and reconstruction of multidimensional surgical information in a digital operation room environment PhD thesis, Va. Commonw. Univ., Richmond
  110. Chmarra MK, Grimbergen CA, Dankelman J. 110.  2007. Systems for tracking minimally invasive surgical instruments. Minim. Invasive Ther. Allied Technol. 16:328–40 [Google Scholar]
  111. DiMaio S, Hasser C. 111.  2008. The da Vinci research interface. MIDAS J. http://hdl.handle.net/10380/1464
  112. Trejos AL, Patel RV, Naish MD, Lyle AC, Schlachta CM. 112.  2009. A sensorized instrument for skills assessment and training in minimally invasive surgery. J. Med. Devices 3:041002 [Google Scholar]
  113. Rutherford DN, D'Angelo ALD, Law KE, Pugh CM. 113.  2015. Advanced engineering technology for measuring performance. Surg. Clin. N. Am. 95:813–26 [Google Scholar]
  114. Kirby GSJ, Guyver P, Strickland L, Alvand A, Yang GZ. 114.  2015. Assessing arthroscopic skills using wireless elbow-worn motion sensors. J. Bone Joint Surg. Am. 97:1119–27 [Google Scholar]
  115. King RC, Atallah L, Lo BPL, Yang GZ. 115.  2009. Development of a wireless sensor glove for surgical skills assessment. IEEE Trans. Inf. Technol. Biomed. 13:673–79 [Google Scholar]
  116. Saggio G, Santosuosso GL, Cavallo P, Pinto CA, Petrella M. 116.  2011. Gesture recognition and classification for surgical skill assessment. Proceedings of the IEEE International Workshop on Medical Measurements662–66 Piscataway, NJ: IEEE
  117. Kodama H, Tercero C, Ooe K, Shi C, Ikeda S. 117.  2012. 2-D optical encoding of catheter motion and cyber-physical system for technical skills measurement and quantitative evaluation in endovascular surgery. Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems3565–70 Piscataway, NJ: IEEE
  118. Conditt MA, Noble PC, Thompson MT, Ismaily SK, Moy GJ, Mathis KB. 118.  2007. A computerized bioskills system for surgical skills training in total knee replacement. Proc. Inst. Mech. Eng. H 221:61–69 [Google Scholar]
  119. Lu J, Kowalewski TM. 119.  2015. Flexible, stretchable skin sensors for two-dimensional position tracking in medical simulators. J. Med. Devices 9:020927 [Google Scholar]
  120. Atkins MS, Tien G, Khan RSA, Meneghetti A, Zheng B. 120.  2012. What do surgeons see: capturing and synchronizing eye gaze for surgery applications. Surg. Innov. 20:241–48 [Google Scholar]
  121. Tien T, Pucher PH, Sodergren MH, Sriskandarajah K, Yang GZ, Darzi A. 121.  2015. Differences in gaze behaviour of expert and junior surgeons performing open inguinal hernia repair. Surg. Endosc. 29:405–13 [Google Scholar]
  122. Pandya A, Reisner LA, King B, Lucas N, Composto A. 122.  2014. A review of camera viewpoint automation in robotic and laparoscopic surgery. Robotics 3:310–29 [Google Scholar]
  123. Morris MC, Frodl T, D'Souza A, Fagan AJ, Ridgway PF. 123.  2015. Assessment of competence in surgical skills using functional magnetic resonance imaging: a feasibility study. J. Surg. Ed. 72:198–204 [Google Scholar]
  124. Evans AW, Leeson RMA, Petrie A. 124.  2005. Correlation between a patient-centred outcome score and surgical skill in oral surgery. Br. J. Oral Maxillofac. Surg. 43:505–10 [Google Scholar]
  125. Ahmidi N. 125.  2015. Activity detection and skill assessment for dexterous motions in robotic and minimally-invasive surgery PhD thesis, Johns Hopkins Univ., Baltimore, Md.
  126. Bergland GD. 126.  1969. A guided tour of the fast Fourier transform. IEEE Spectrum 6:41–52 [Google Scholar]
  127. Zhang D, Lu G. 127.  2004. Review of shape representation and description techniques. Pattern Recognit. 37:1–19 [Google Scholar]
  128. Laptev I, Marszalek M, Schmid C, Rozenfeld B. 128.  2008. Learning realistic human actions from movies. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition1–8 Piscataway, NJ: IEEE
  129. Ershad M, Koesters Z, Rege R, Majewicz A. 129.  2016. Meaningful assessment of surgical expertise: semantic labeling with data and crowds. In Medical Image Computing and Computer-Assisted Intervention S Ourselin, L Joskowicz, MR Sabuncu, G Unal, W Wells 508–15 Berlin: Springer [Google Scholar]
  130. Sant'Anna A, Wickström N. 130.  2011. Symbolization of time-series: an evaluation of SAX, Persist, and ACA. Proceedings of the 4th International Congress on Image and Signal Processing2223–28 Piscataway, NJ: IEEE
  131. Shafiei SB, Guru KA, Esfahani ET. 131.  2015. Using two-third power law for segmentation of hand movement in robotic assisted surgery. Proceedings of the 2015 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: 39th Mechanisms and Robotics Conference pap. V05CT08A014 Boston: ASME
  132. Bettadapura V, Schindler G, Ploetz T, Essa I. 132.  2013. Augmenting bag-of-words: data-driven discovery of temporal and structural information for activity recognition. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition2619–26 Piscataway, NJ: IEEE
  133. Jelinek F. 133.  2001. Statistical Methods for Speech Recognition Cambridge, MA: MIT Press
  134. Porte MC, Xeroulis G, Reznick RK, Dubrowski A. 134.  2007. Verbal feedback from an expert is more effective than self-accessed feedback about motion efficiency in learning new surgical skills. Am. J. Surg. 193:105–10 [Google Scholar]
  135. Bjerrum F, Maagaard M, Sorensen JL, Larsen CR, Ringsted C. 135.  2015. Effect of instructor feedback on skills retention after laparoscopic simulator training: follow-up of a randomized trial. J. Surg. Ed. 72:53–60 [Google Scholar]
/content/journals/10.1146/annurev-bioeng-071516-044435
Loading
/content/journals/10.1146/annurev-bioeng-071516-044435
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error