Enhancement of surgical training practice with the spring tensor heuristic model

Chiu, C; Chaczko, Z

Enhancement of surgical training practice with the spring tensor heuristic model

Chiu, C

Chaczko, Z

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Publication Type:: Journal Article
Citation:: International Journal of Electronics and Telecommunications, 2013, 59 (3), pp. 237 - 244
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Chiu, C https://orcid.org/0000-0003-2045-5961	en_US
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	International Journal of Electronics and Telecommunications, 2013, 59 (3), pp. 237 - 244	en_US
dc.identifier.issn	2081-8491	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27159
dc.description.abstract	The enhancement of surgical simulation tools is an important research study, to assist in the assessment and feedback of medical training practice. In this research, the Spring Tensor Model (STEM) has been used for laparoscopic end-effector navigation through obstacles and high-risk areas. The modelling of the surgical trainer as part of the laparoscopic simulator seeks to emulate the physical environment as a virtualised representation in the integrated infrastructure. Combining sensor network framework paradigms to a surgical knowledge-based construct demonstrates how STEM can enhance medical practice. The architectural hybridisation of the training framework has enabled the adaptation of STEM modelling techniques for a simulated laparoscopic training methodology. The primary benefit of the architecture is that this integration strategy has resulted in a seamless transition of the heuristic framework to be applied to surgical training.	en_US
dc.relation.ispartof	International Journal of Electronics and Telecommunications	en_US
dc.relation.isbasedon	10.2478/eletel-2013-0028	en_US
dc.title	Enhancement of surgical training practice with the spring tensor heuristic model	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	59	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Architecture
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	59	en_US

Abstract:

The enhancement of surgical simulation tools is an important research study, to assist in the assessment and feedback of medical training practice. In this research, the Spring Tensor Model (STEM) has been used for laparoscopic end-effector navigation through obstacles and high-risk areas. The modelling of the surgical trainer as part of the laparoscopic simulator seeks to emulate the physical environment as a virtualised representation in the integrated infrastructure. Combining sensor network framework paradigms to a surgical knowledge-based construct demonstrates how STEM can enhance medical practice. The architectural hybridisation of the training framework has enabled the adaptation of STEM modelling techniques for a simulated laparoscopic training methodology. The primary benefit of the architecture is that this integration strategy has resulted in a seamless transition of the heuristic framework to be applied to surgical training.

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http://hdl.handle.net/10453/27159