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Surgery is one of the riskiest and most important medical acts that are performed today. The need to improve patient outcomes and surgeon training, and to reduce the costs of surgery, has motivated the equipment of operating rooms with sensors that record surgical interventions. The richness and complexity of the data that are collected call for new methods to support computer-assisted surgery. The aim of this paper is to support the monitoring of junior surgeons learning their surgical skill sets.
Our method is fully automatic and takes as input a series of surgical interventions each represented by a low-level recording of all activities performed by the surgeon during the intervention (e.g., cut the skin with a scalpel). Our method produces a curve describing the process of standardization of the behavior of junior surgeons. Given the fact that junior surgeons receive constant feedback from senior surgeons during surgery, these curves can be directly interpreted as learning curves.
Our method is assessed using the behavior of a junior surgeon in anterior cervical discectomy and fusion surgery over his first three years after residency. They revealed the ability of the method to accurately represent the surgical skill evolution. We also showed that the learning curves can be computed by phases allowing a finer evaluation of the skill progression.
Preliminary results suggest that our approach constitutes a useful addition to surgical training monitoring.
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Arora KS, Khan N, Abboudi H, Khan MS, Dasgupta P, Ahmed K (2015) Learning curves for cardiothoracic and vascular surgical procedures-a systematic review. Postgrad Med 127(2):202–214
Barrie J, Jayne DG, Wright J, Murray CJC, Collinson FJ, Pavitt SH (2014) Attaining surgical competency and its implications in surgical clinical trial design: a systematic review of the learning curve in laparoscopic and robot-assisted laparoscopic colorectal cancer surgery. Ann Surg Oncol 21(3):829–840 CrossRefPubMed
Dewey RA (2007) Psychology: an introduction. Russ Dewey
Forestier G, Riffaud L, Jannin P (2015) Automatic phase prediction from low-level surgical activities. Int J Comput Assist Radiol Surg 10(6):833–841
Hanzly M, Frederick A, Creighton T, Atwood K, Mehedint D, Kauffman EC, Kim HL, Schwaab T (2014) Learning curves for robot-assisted and laparoscopic partial nephrectomy. J Endourol 20:297–303
Lalys F, Jannin P (2013) Surgical process modelling: a review. Int J Comput Assist Radiol Surg 8(5):1–17
Lalys F, Riffaud L, Morandi X, Jannin P (2010) Automatic phases recognition in pituitary surgeries by microscope images classification. In: Information processing in computer-assisted interventions. Springer, pp 34–44
Lin HC, Shafran I, Murphy TE, Okamura AM, Yuh DD, Hager GD (2005) Automatic detection and segmentation of robot-assisted surgical motions. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2005. Springer, pp 802–810
MacKenzie L, Ibbotson J, Cao C, Lomax A (2001) Hierarchical decomposition of laparoscopic surgery: a human factors approach to investigating the operating room environment. Minim Invasive Therapy Allied Technol 10(3):121–127 CrossRef
Meißner C, Meixensberger J, Pretschner A, Neumuth T (2014) Sensor-based surgical activity recognition in unconstrained environments. Minim Invasive Therapy Allied Technol 23:198–205 CrossRef
Neumuth T, Durstewitz N, Fischer M, Strauß G, Dietz A, Meixensberger J, Jannin P, Cleary K, Lemke HU, Burgert O (2006) Structured recording of intraoperative surgical workflows. In: Medical imaging. International Society for Optics and Photonics, pp 61450A–61450A
PARK1a S-H, Suh IH, Chien J-h, Paik J, Ritter FE, Oleynikov D, Siu K-C (2011) Modeling surgical skill learning with cognitive simulation. Medi Meets Virtual Real 18: NextMed, 163:428
Ritter FE, Schooler LJ (2001) The learning curve. Int Encycl Social Behav Sci 13:8602–8605
Rodriguez-Paz J, Kennedy M, Salas E, Wu A, Sexton J, Hunt E, Pronovost P (2009) Beyond “see one, do one, teach one” : toward a different training paradigm. Qual Saf Health Care 18(1):63–68 PubMed
Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49 CrossRef
Sharma Y, Plötz T, Hammerld N, Mellor S, McNaney R, Olivier P, Deshmukh S, McCaskie A, Essa I (2014) Automated surgical osats prediction from videos. In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). IEEE, pp 461–464
Varadarajan B, Reiley C, Lin H, Khudanpur S, Hager G (2009) Data-derived models for segmentation with application to surgical assessment and training. In Medical Image Computing and Computer-Assisted Intervention—MICCAI 2009. Springer, pp 426–434
Wright TP (2012) Factors affecting the cost of airplanes. J Aeronaut Sci (Inst Aeronaut Sci) 3(4):122–128 CrossRef
Yelle LE (1979) The learning curve: historical review and comprehensive survey. Decis Sci 10(2):302–328 CrossRef
- Surgical skills: Can learning curves be computed from recordings of surgical activities?
- Springer International Publishing
International Journal of Computer Assisted Radiology and Surgery
A journal for interdisciplinary research, development and applications of image guided diagnosis and therapy
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
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