Relevant bibliographies by topics / Surgical Skill

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Surgical Skill'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Surgical Skill"

1

Khan, M. S., S. D. Bann, A. Darzi, and P. E. M. Butler. "Assessing Surgical Skill." Plastic and Reconstructive Surgery 112, no. 7 (December 2003): 1886–89. http://dx.doi.org/10.1097/01.prs.0000091244.89368.3b.

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Golnik, Karl C., Hilary Beaver, Vinod Gauba, Andrew G. Lee, Eduardo Mayorga, Gabriela Palis, and George M. Saleh. "Cataract Surgical Skill Assessment." Ophthalmology 118, no. 2 (February 2011): 427–427. http://dx.doi.org/10.1016/j.ophtha.2010.09.023.

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Mishriki, S. F. "NICE forgot surgical skill." BMJ 337, no. 19 1 (November 19, 2008): a2579. http://dx.doi.org/10.1136/bmj.a2579.

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Byrne, Michelle. "Skill Acquisition." AORN Journal 43, no. 6 (June 1986): 1312–17. http://dx.doi.org/10.1016/s0001-2092(07)65161-8.

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Liang, Hui, and Min Yong Shi. "Surgical Skill Evaluation Model for Virtual Surgical Training." Applied Mechanics and Materials 40-41 (November 2010): 812–19. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.812.

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As a safe, cost-effective, and easily accessible tool for gaining experience in surgery, the simulation-based surgical skill training has attracted more and more attention in modern hospitals and institutes. One of the most important advantages of virtual surgical training is affording useful instructional feedback to users. However, how to provide systemic and competitive surgical technique evaluation to trainees is still untouched. In this paper, for UK’s Royal Bournemouth Hospital virtual surgery system, we creatively constructed a surgical technique evaluation model which consists of static structure and dynamic process, as well as an index system which covers aspects of surgery performance.
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Perez, Ray S., Anna Skinner, Peter Weyhrauch, James Niehaus, Corinna Lathan, Steven D. Schwaitzberg, and Caroline G. L. Cao. "Prevention of Surgical Skill Decay." Military Medicine 178, no. 10S (October 2013): 76–86. http://dx.doi.org/10.7205/milmed-d-13-00216.

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Siska, Van Bruwaene, Lissens Ann, De Win Gunter, Neyrinck Bart, Lens Willy, Schijven Marlies, and Miserez Marc. "Surgical Skill: Trick or Trait?" Journal of Surgical Education 72, no. 6 (November 2015): 1247–53. http://dx.doi.org/10.1016/j.jsurg.2015.05.004.

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Lendvay, Thomas S., Lee White, and Timothy Kowalewski. "Crowdsourcing to Assess Surgical Skill." JAMA Surgery 150, no. 11 (November 1, 2015): 1086. http://dx.doi.org/10.1001/jamasurg.2015.2405.

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Ori, Michele, Luca D'Ascanio, and Giampietro Ricci. "Daytime Sleepiness and Surgical Skill." JAMA Facial Plastic Surgery 21, no. 4 (July 2019): 343–44. http://dx.doi.org/10.1001/jamafacial.2018.2040.

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Phillips, Robert. "Cognitive scores measure surgical skill." Nature Reviews Urology 11, no. 3 (February 11, 2014): 130. http://dx.doi.org/10.1038/nrurol.2014.26.

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Dissertations / Theses on the topic "Surgical Skill"

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Cristancho, Sayra Magnolia. "Quantitative modelling and assessment of surgical motor actions in minimally invasive surgery." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/2835.

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The goal of this research was to establish a methodology for quantifying performance of surgeons and distinguishing skill levels during live surgeries. We integrated three physical measures (kinematics, time and movement transitions) into a modeling technique for quantifying performance of surgical trainees. We first defined a new hierarchical representation called Motor and Cognitive Modeling Diagram for laparoscopic procedures, which: (1) decomposes ‘tasks’ into ‘subtasks’ and at the very detailed level into individual movements ‘actions’; and (2) includes an explicit cognitive/motor diagrammatic representation that enables to take account of the operative variability as most intraoperative assessments are conducted at the ‘whole procedure’ level and do not distinguish between performance of trivial and complicated aspects of the procedure. Then, at each level of surgical complexity, we implemented specific mathematical techniques for providing a quantitative sense of how far a performance is located from a reference level: (1) The Kolgomorov-Smirnov statistic to describe the similarity between two empirical cumulative distribution functions (e.g., speed profiles) (2) The symmetric normalized Jensen-Shannon Divergence to compare transition probability matrices (3) The Principal Component Analysis to identify the directions of greatest variability in a multidimensional space and to reduce the dimensionality of the data using a weight space. Two experimental studies were completed in order to show feasibility of our proposed assessment methodology by monitoring movements of surgical tools while: (1) dissecting mandarin oranges, and (2) performing laparoscopic cholecystectomy procedures at the operating room to compare residents and expert surgeons when executing two surgical tasks: exposing Calot’s Triangle and dissecting the cystic duct and artery. Results demonstrated the ability of our methodology to represent selected tasks using the Motor and Cognitive Modeling Diagram and to differentiate skill levels. We aim to use our approach in future studies to establish correspondences between specific surgical tasks and the corresponding simulations of these tasks, which may ultimately enable us to do validated assessments in a simulated setting, and to test its reliability in differentiating skill levels at the operating room as the number of subjects and procedures increase.
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Sharma, Yachna. "Surgical skill assessment using motion texture analysis." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51890.

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In this thesis, we propose a framework for automated assessment of surgical skills to expedite the manual assessment process and to provide unbiased evaluations with possible dexterity feedback. Evaluation of surgical skills is an important aspect in training of medical students. Current practices rely on manual evaluations from faculty and residents and are time consuming. Proposed solutions in literature involve retrospective evaluations such as watching the offline videos. It requires precious time and attention of expert surgeons and may vary from one surgeon to another. With recent advancements in computer vision and machine learning techniques, the retrospective video evaluation can be best delegated to the computer algorithms. Skill assessment is a challenging task requiring expert domain knowledge that may be difficult to translate into algorithms. To emulate this human observation process, an appropriate data collection mechanism is required to track motion of the surgeon's hand in an unrestricted manner. In addition, it is essential to identify skill defining motion dynamics and skill relevant hand locations. This Ph.D. research aims to address the limitations of manual skill assessment by developing an automated motion analysis framework. Specifically, we propose (1) to design and implement quantitative features to capture fine motion details from surgical video data, (2) to identify and test the efficacy of a core subset of features in classifying the surgical students into different expertise levels, (3) to derive absolute skill scores using regression methods and (4) to perform dexterity analysis using motion data from different hand locations.
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Ratnasothy, Joel. "Assessing surgical skill for procedures on the conscious patient." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.504909.

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Scialoja, Alain. "Skill-based shared manipulation control of eye-surgical system." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5805/.

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Datta, Vivek Kumar. "The objective assessment of skill in higher surgical training." Thesis, Imperial College London, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.397243.

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Khan, Mansoor Shahid. "The objective assessment of surgical skill in plastic surgeons." Thesis, Imperial College London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428709.

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Mackay, Sean Desmond Patrick. "The objective assessment of technical skill in basic surgical trainees." Thesis, Imperial College London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407597.

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Shime, Jerry. "Reliability study of the Laparoscopic Skills Index, LSI, a new measure of gynecologic laparoscopic surgical skill." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0018/MQ53478.pdf.

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Kume, Naoto. "Distributed massive simulation for haptic virtual reality based surgical skill transfer." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/135940.

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Smith, Phillip R. "Instrument tracking and analysis in minimal access surgery for surgical skill assessment." Thesis, University of Surrey, 2016. http://epubs.surrey.ac.uk/809462/.

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For this project, we analyse cataract surgery videos. It is known that the motions of both camera and surgical instruments are indicative of surgical skill in simulated environments. Through the application of computer vision algorithms, we attempt to automatically measure these motions. Video data from cataract surgery videos have many sources of noise that complicate the observation of such motion. As no 'de facto' method exists for tracking surgical instruments we investigate the validity of applying cues based upon colour, shape and motion to identify surgical instruments. In addition, we develop a iris tracker based upon Histogram of Gradients object detection to measure the changes in camera state throughout a procedure. A methodology based upon invariant characteristics of surgical instrument motion is developed and applied to a large dataset of procedures. Metrics such as path length and number of motions for surgical instruments in cataract surgery are measured with this fully automatic methodology. Path length and number of movements are compared with surgeon's experience and skill level as measured with a manual surgical skill marking scheme. These metrics are shown to be proportional to a surgeon's experience and in agreement with manual measures of surgical skill.
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Books on the topic "Surgical Skill"

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Sherris, David A. Essential surgical skills. 2nd ed. Philadelphia, Pa: Curtis Center, 2004.

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Basic surgical skills manual. Sidney, Australia: McGraw-Hill, 2000.

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1937-, Buerk Charles A., ed. Pocket manual of basic surgical skills. St. Louis: C.V. Mosby, 1986.

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J, Busch Sara, ed. Small animal surgical nursing: Skills and concepts. 2nd ed. St. Louis, Mo: Elsevier/Mosby, 2012.

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Busch, Sara J. Small animal surgical nursing: Skills and concepts. St. Louis, Mo: Mosby, 2005.

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Operating room skills: Fundamentals for the surgical technologist. Boston: Pearson, 2013.

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Kinesh, Patel, ed. Complete OSCE skills for medical and surgical finals. London: Hodder Arnold, 2010.

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Surgical anatomy of the head and neck. Cambridge, Mass: Harvard University Press, 2011.

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Seth, Rosenberg, ed. Surgical techniques of the temporal bone and skull base. Philadelphia: Lea & Febiger, 1992.

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Fliss, Dan M., and Ziv Gil. Atlas of Surgical Approaches to Paranasal Sinuses and the Skull Base. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-48632-0.

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Book chapters on the topic "Surgical Skill"

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Gallagher, Anthony G., Gerald C. O’Sullivan, and Gerald C. O’Sullivan. "Metrics for the Measurement of Skill." In Fundamentals of Surgical Simulation, 123–53. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-763-1_5.

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Gallagher, Anthony G., Gerald C. O’Sullivan, and Gerald C. O’Sullivan. "Human Factors in Acquiring Medical Skills; Learning and Skill Acquisition in Surgery." In Fundamentals of Surgical Simulation, 89–121. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-763-1_4.

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Causey, Marlin Wayne, and Robert M. Rush. "Skill Maintenance, Remediation, and Reentry." In Comprehensive Healthcare Simulation: Surgery and Surgical Subspecialties, 79–87. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98276-2_8.

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Gallagher, Anthony G., Gerald C. O’Sullivan, and Gerald C. O’Sullivan. "Human Factors in Acquiring Medical Skill; Perception and Cognition." In Fundamentals of Surgical Simulation, 67–87. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-763-1_3.

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Jaksa, László, Illés Nigicser, Balázs Szabó, Dénes Ákos Nagy, Péter Galambos, and Tamás Haidegger. "CogInfoCom-Driven Surgical Skill Training and Assessment." In Topics in Intelligent Engineering and Informatics, 277–304. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95996-2_13.

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Wang, Tianyu, Yijie Wang, and Mian Li. "Towards Accurate and Interpretable Surgical Skill Assessment: A Video-Based Method Incorporating Recognized Surgical Gestures and Skill Levels." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 668–78. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59716-0_64.

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Benmansour, Malik, Wahida Handouzi, and Abed Malti. "Task-Specific Surgical Skill Assessment with Neural Networks." In Advances in Intelligent Systems and Computing, 159–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11884-6_15.

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Costantini, Giovanni, Giovanni Saggio, Laura Sbernini, Nicola Di Lorenzo, and Daniele Casali. "Towards an Objective Tool for Evaluating the Surgical Skill." In Studies in Computational Intelligence, 325–35. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26393-9_19.

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Islam, Gazi, Kanav Kahol, John Ferrara, and Richard Gray. "Development of Computer Vision Algorithm for Surgical Skill Assessment." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 44–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23902-1_6.

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Rissanen, Mikko, Yoshihiro Kuroda, Megumi Nakao, Naoto Kume, Tomohiro Kuroda, and Hiroyuki Yoshihara. "Annotated Surgical Manipulation for Simulator-Based Surgical Skill-Transfer Using SiRE – Simulation Record Editor." In Biomedical Simulation, 122–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11790273_14.

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Conference papers on the topic "Surgical Skill"

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Harada, K., Y. Minakawa, Y. Baek, Y. Kozuka, S. Sora, A. Morita, N. Sugita, and M. Mitsuishi. "Microsurgical skill assessment: Toward skill-based surgical robotic control." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091652.

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Nemani, Arun, Suvranu De, and Xavier Intes. "Functional Brain Connectivity Distinguishes Surgical Skill Learning with Surgical Simulators." In Clinical and Translational Biophotonics. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/translational.2018.jth3a.53.

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Nemani, Arun, Suvranu De, and Xavier Intes. "Objective Assessment of Surgical Skill with fNIRS." In Conference on Lasers and Electro-Optics/Pacific Rim. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/cleopr.2018.w1k.2.

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Long, Steven, Geb W. Thomas, and Donald D. Anderson. "Designing an Extensible Wire Navigation Simulation Platform." In 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3435.

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Orthopaedic resident training has been, and continues to be, in a state of flux. Initially, there were limits placed on the number of hours a resident could work in a week [1]. Later, residency programs were required to provide laboratory-based training in basic surgical skill for first year residents [2]. Now there is a push towards a competency-based training program that graduates residents who demonstrate their acquisition of adequate surgical skills [3]. With each of these shifts in the training model, programs and institutions have looked increasingly to simulation-based training to ease the way. Simulation offers opportunities to train surgeons quickly, provide essential feedback to foster improvement, and assess skill acquisition. With the broad swath of requirements to satisfy in orthopaedic surgical skills training, a simulation platform must support an array of training capabilities for resident practice and performance assessment. Wire navigation is a central skill in orthopaedics that has a broad variety of applications. In this task, surgeons must use 2D intra-operative fluoroscopic images to visualize the 3D anatomy of a patient and place a wire along a specified path through bone. In some situations, placing the wire is the final task; in others the wire serves as a guide for subsequently placed cannulated implants. Regardless of the situation, the placement of the wire in the bone directly influences the surgical result for the patient. We previously presented the design of a wire navigation surgical simulator dedicated specifically to hip wire navigation [4]. Our experience with the dozens of surgeons and residents who have used the simulator suggest that they find the general skill of guiding a wire to be relatively abstract. They are more drawn to practicing specific surgeries rather than the general skill. To address this need, we have modified the simulator to present new surgical procedures, while still exercising the underlying skill of wire navigation. We also learned that the task of directing the fluoroscope in order to acquire appropriate view angles for making surgical decisions is integral to surgical wire navigation, so we extended the simulator to include this important aspect of surgical skill.
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Wijayarathne, Lasitha, and Frank L. Hammond. "Kinetic Measurement Platform for Open Surgical Skill Assessment." In 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3525.

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Current surgical skill assessment methods are often based on the kinematics of manual surgical instruments during tool-tissue interactions. Though kinematic data are generally regarded as a sufficient basis for skill assessment, the inclusion of kinetic information would allow the assessment of measures such as “respect for tissue” and force control, which are also important aspects of surgical proficiency. Kinetic data would also provide a richer data set upon which automated surgical motion segmentation and classification algorithms can be developed. However, the kinetics of tool-tissue interactions are seldom included in assessments, due largely to the difficulty of mounting small sensors — typically silicon strain gauges — onto surgical instruments to capture force data. Electromagnetic (EM) or optical trackers used for kinematic measurement are often tethered, and thus having tethered force sensors also mounted on the same surgical instruments would complicate the experimental process and could affect/distort the acquired data by impeding the natural manual motions of surgeons. We present a surgical skill assessment platform which places the kinetic sensors in the environment, not on the instruments, to reduce the physical encumbrance of the system to the surgeon. This system can capture kinetic data using a standalone force/torque sensor embedded in a custom designed workspace platform, and kinematic data using EM trackers placed on the instruments. This portable platform enables the empirical characterization of open surgery motion trajectories and corresponding kinetic data without need for a centralized acquisition site, and will eventually be integrated into a completely untethered skill assessment system.
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Safavi, Ali, and Mehrdad H. Zadeh. "Model-Based Haptic Guidance in Surgical Skill Improvement." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.198.

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Albasri, Safaa, Mihail Popescu, and James Keller. "A Novel Distance for Automated Surgical Skill Evaluation." In 2019 E-Health and Bioengineering Conference (EHB). IEEE, 2019. http://dx.doi.org/10.1109/ehb47216.2019.8970029.

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Ershad, Marzieh, Robert Rege, and Ann Majewicz Fey. "Automatic Surgical Skill Rating Using Stylistic Behavior Components." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8512593.

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Saggio, G., G. L. Santosuosso, P. Cavallo, C. A. Pinto, M. Petrella, F. Giannini, N. Di Lorenzo, et al. "Gesture recognition and classification for surgical skill assessment." In 2011 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2011. http://dx.doi.org/10.1109/memea.2011.5966681.

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Jian, Zhiteng, Wenxi Yue, Qiuxia Wu, Wei Li, Zhiyong Wang, and Vincent Lam. "Multitask Learning for Video-based Surgical Skill Assessment." In 2020 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2020. http://dx.doi.org/10.1109/dicta51227.2020.9363408.

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Reports on the topic "Surgical Skill"

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Wu, Ming-Kung, and Ping-Tao Tseng. The efficacy of transcranial direct current stimulation in enhancing surgical skill acquisition. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2021. http://dx.doi.org/10.37766/inplasy2021.4.0099.

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Satava, Richard M. Metrics for Objective Assessment of Surgical Skills Workshop. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada407534.

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Mackenzie, Colin, Evan Garofalo, Stacy Shackelford, Mark Bowyer, Sharon Henry, Kostantinos Kalipakis, and Megan Holmes. Use of Performance Measures to Evaluate, Document Competence and Deterioration of Advanced Surgical Skills Exposure for Trauma (ASSET) Surgical Skills. Fort Belvoir, VA: Defense Technical Information Center, March 2014. http://dx.doi.org/10.21236/ada612985.

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Zadinsky, Julie K. The Readiness Training Program for Nursing Personnel in the AMEDD. Volume 3A. Training Manual to Accompany the Videotape: Readiness Training in Medical-Surgical Nursing Skills. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada301218.

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