Development of an intelligent surgical training system for Thoracentesis

[1]  Giancarlo Ferrigno,et al.  Inductive Learning of the Surgical Workflow Model through Video Annotations , 2017, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS).

[2]  Giancarlo Ferrigno,et al.  Toward a Knowledge-Driven Context-Aware System for Surgical Assistance , 2017, J. Medical Robotics Res..

[3]  J. M. Porcel,et al.  Una encuesta a médicos residentes sobre la realización de toracocentesis diagnósticas y terapéuticas: ¿una laguna en la formación? , 2016 .

[4]  J. M. Porcel,et al.  A survey to medical residents on the performance of diagnostic and therapeutic thoracenteses: a training gap? , 2016, Revista clinica espanola.

[5]  J. Barsuk,et al.  The effect of simulation-based mastery learning on thoracentesis referral patterns. , 2016, Journal of hospital medicine.

[6]  Giancarlo Ferrigno,et al.  Gesteme-free context-aware adaptation of robot behavior in human-robot cooperation , 2016, Artif. Intell. Medicine.

[7]  Ian Kamaly,et al.  Simulation in neurosurgical training: a blueprint and national approach to implementation for initial years trainees , 2016, British Journal of Neurosurgery.

[8]  Rüdiger Dillmann,et al.  LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition , 2015, International Journal of Computer Assisted Radiology and Surgery.

[9]  J. McSparron,et al.  Simulation for Skills-based Education in Pulmonary and Critical Care Medicine. , 2015, Annals of the American Thoracic Society.

[10]  J. R. Mason,et al.  Murray and Nadel's Textbook of Respiratory Medicine , 2015 .

[11]  J. Wojtczak Models to teach lung sonopathology and ultrasound-guided thoracentesis , 2014, Journal of ultrasonography.

[12]  Giancarlo Ferrigno,et al.  Automatic classification of epilepsy types using ontology-based and genetics-based machine learning , 2014, Artif. Intell. Medicine.

[13]  Francisco José Madrid-Cuevas,et al.  Automatic generation and detection of highly reliable fiducial markers under occlusion , 2014, Pattern Recognit..

[14]  Christophe Cruz,et al.  Knowledge Base Approach for 3D Objects Detection in Point Clouds Using 3D Processing and Specialists Knowledge , 2013, ArXiv.

[15]  V. Aiyappan,et al.  Junior doctor training in pleural procedures: a quality survey , 2010, Internal medicine journal.

[16]  Gaurav S. Sukhatme,et al.  Using manipulation primitives for brick sorting in clutter , 2012, 2012 IEEE International Conference on Robotics and Automation.

[17]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[18]  Anghel Leonard Watch Service API , 2011 .

[19]  Sean Bechhofer,et al.  The OWL API: A Java API for OWL ontologies , 2011, Semantic Web.

[20]  Daniele Braga,et al.  Querying RDF streams with C-SPARQL , 2010, SGMD.

[21]  Ryan R Brinkman,et al.  OntoFox: web-based support for ontology reuse , 2010, BMC Research Notes.

[22]  G Ferrigno,et al.  Robotic and artificial intelligence for keyhole neurosurgery: The ROBOCAST project, a multi-modal autonomous path planner , 2010, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[23]  Paul Anderson,et al.  Essential JavaFX , 2009 .

[24]  E. Matsumoto,et al.  Assessing the surgical decision making abilities of novice and proficient urologists. , 2007, The Journal of urology.

[25]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[26]  Nico Blodow,et al.  Towards 3D Point cloud based object maps for household environments , 2008, Robotics Auton. Syst..

[27]  S. Faruqi,et al.  Winging of the scapula: An unusual complication of needle thoracocentesis. , 2008, European journal of internal medicine.

[28]  Michael Kipp Spatiotemporal Coding in ANVIL , 2008, LREC.

[29]  Rhona Flin,et al.  Safety at the Sharp End: A Guide to Non-Technical Skills , 2008 .

[30]  Cornelius Rosse,et al.  The Foundational Model of Anatomy Ontology , 2008, Anatomy Ontologies for Bioinformatics.

[31]  Monique Thonnat,et al.  Ontology based complex object recognition , 2008, Image Vis. Comput..

[32]  Rhona Flin,et al.  How do surgeons make intraoperative decisions? , 2007, Quality and Safety in Health Care.

[33]  Yarden Katz,et al.  Pellet: A practical OWL-DL reasoner , 2007, J. Web Semant..

[34]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[35]  T. Thomsen,et al.  Videos in clinical medicine. Thoracentesis. , 2006, The New England journal of medicine.

[36]  Gero Strauß,et al.  Acquisition of Process Descriptions from Surgical Interventions , 2006, DEXA.

[37]  Adam Dubrowski,et al.  Teaching Surgical Skills: What Kind of Practice Makes Perfect?: A Randomized, Controlled Trial , 2006, Annals of surgery.

[38]  Kevin Cleary,et al.  OR 2020 workshop report: Operating room of the future , 2005 .

[39]  J. R. Quinlan Learning Logical Definitions from Relations , 1990 .

[40]  Barry Smith,et al.  SNAP and SPAN: Towards Dynamic Spatial Ontology , 2004, Spatial Cogn. Comput..

[41]  H. Lan,et al.  SWRL : A semantic Web rule language combining OWL and ruleML , 2004 .

[42]  S. Paterson-Brown,et al.  Trainees' opinions of the skills required of basic surgical trainees. , 2003, American journal of surgery.

[43]  David Ascher,et al.  Python Cookbook , 2002 .

[44]  Dieter Fox,et al.  KLD-Sampling: Adaptive Particle Filters , 2001, NIPS.

[45]  N. F. Noy,et al.  Ontology Development 101: A Guide to Creating Your First Ontology , 2001 .

[46]  P J Baldwin,et al.  Consultant surgeons' opinion of the skills required of basic surgical trainees , 1999, The British journal of surgery.

[47]  Wolfram Burgard,et al.  Using the CONDENSATION algorithm for robust, vision-based mobile robot localization , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[48]  M. Berthod,et al.  Automatic classification of planktonic foraminifera by a knowledge-based system , 1994, Proceedings of the Tenth Conference on Artificial Intelligence for Applications.

[49]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.