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Fast pre-clinical in-silico surrogate methods for statistical shape modelling

Fast pre-clinical in-silico surrogate methods for statistical shape modelling
Fast pre-clinical in-silico surrogate methods for statistical shape modelling
In both industrial and clinical use, contemporary pre-clinical analysis methods place increasing emphasis on a more ‘holistic’ understanding of performance, accounting for a wider range of factors. This places a heavy time-burden on pre-clinical analysis methods, with wide-ranging stochastic methods, sweep studies or sensitivity analysis methods requiring extended processing time. In tension with this, there is an expectation that, in order to be practically useful, any computational support tool should be responsive, delivering results more quickly or even near-instantaneously for ‘real-time’ clinical use. The broad challenge is to make these models fast, accessible and relevant for end-users in a clinical and/or industrial context
Strickland, Michael A.
6b639de6-cb09-4383-bf06-576eb6aef448
Taylor, Mark
e368bda3-6ca5-4178-80e9-41a689badeeb
Strickland, Michael A.
6b639de6-cb09-4383-bf06-576eb6aef448
Taylor, Mark
e368bda3-6ca5-4178-80e9-41a689badeeb

Strickland, Michael A. and Taylor, Mark (2011) Fast pre-clinical in-silico surrogate methods for statistical shape modelling. Engineers and Surgeons: Joined at the Hip III, London, United Kingdom. 01 - 03 Nov 2011. 3 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

In both industrial and clinical use, contemporary pre-clinical analysis methods place increasing emphasis on a more ‘holistic’ understanding of performance, accounting for a wider range of factors. This places a heavy time-burden on pre-clinical analysis methods, with wide-ranging stochastic methods, sweep studies or sensitivity analysis methods requiring extended processing time. In tension with this, there is an expectation that, in order to be practically useful, any computational support tool should be responsive, delivering results more quickly or even near-instantaneously for ‘real-time’ clinical use. The broad challenge is to make these models fast, accessible and relevant for end-users in a clinical and/or industrial context

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Published date: November 2011
Venue - Dates: Engineers and Surgeons: Joined at the Hip III, London, United Kingdom, 2011-11-01 - 2011-11-03
Organisations: Bioengineering Group

Identifiers

Local EPrints ID: 202765
URI: http://eprints.soton.ac.uk/id/eprint/202765
PURE UUID: e375b815-1e6a-4a11-8c95-9f99e8f36c6f

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Date deposited: 09 Nov 2011 14:53
Last modified: 14 Mar 2024 04:25

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Contributors

Author: Michael A. Strickland
Author: Mark Taylor

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