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Artificial neural networks in pancreatic disease.

Bartosch-Härlid, Anna LU ; Andersson, Bodil LU ; Aho, Ursula LU ; Nilsson, J and Andersson, Roland LU (2008) In British Journal of Surgery 95(7). p.817-826
Abstract
BACKGROUND: An artificial neural network (ANNs) is a non-linear pattern recognition technique that is rapidly gaining in popularity in medical decision-making. This study investigated the use of ANNs for diagnostic and prognostic purposes in pancreatic disease, especially acute pancreatitis and pancreatic cancer. METHODS: PubMed was searched for articles on the use of ANNs in pancreatic diseases using the MeSH terms 'neural networks (computer)', 'pancreatic neoplasms', 'pancreatitis' and 'pancreatic diseases'. A systematic review of the articles was performed. RESULTS: Eleven articles were identified, published between 1993 and 2007. The situations that lend themselves best to analysis by ANNs are complex multifactorial relationships,... (More)
BACKGROUND: An artificial neural network (ANNs) is a non-linear pattern recognition technique that is rapidly gaining in popularity in medical decision-making. This study investigated the use of ANNs for diagnostic and prognostic purposes in pancreatic disease, especially acute pancreatitis and pancreatic cancer. METHODS: PubMed was searched for articles on the use of ANNs in pancreatic diseases using the MeSH terms 'neural networks (computer)', 'pancreatic neoplasms', 'pancreatitis' and 'pancreatic diseases'. A systematic review of the articles was performed. RESULTS: Eleven articles were identified, published between 1993 and 2007. The situations that lend themselves best to analysis by ANNs are complex multifactorial relationships, medical decisions when a second opinion is needed and when automated interpretation is required, for example in a situation of an inadequate number of experts. CONCLUSION: Conventional linear models have limitations in terms of diagnosis and prediction of outcome in acute pancreatitis and pancreatic cancer. Management of these disorders can be improved by applying ANNs to existing clinical parameters and newly established gene expression profiles. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
British Journal of Surgery
volume
95
issue
7
pages
817 - 826
publisher
John Wiley & Sons
external identifiers
  • wos:000257343600004
  • pmid:18551536
  • scopus:47249158871
ISSN
1365-2168
DOI
10.1002/bjs.6239
language
English
LU publication?
yes
id
82c1268a-cee2-4e8a-9944-de2100c82013 (old id 1168839)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/18551536?dopt=Abstract
date added to LUP
2008-07-03 13:05:53
date last changed
2017-10-08 04:29:09
@article{82c1268a-cee2-4e8a-9944-de2100c82013,
  abstract     = {BACKGROUND: An artificial neural network (ANNs) is a non-linear pattern recognition technique that is rapidly gaining in popularity in medical decision-making. This study investigated the use of ANNs for diagnostic and prognostic purposes in pancreatic disease, especially acute pancreatitis and pancreatic cancer. METHODS: PubMed was searched for articles on the use of ANNs in pancreatic diseases using the MeSH terms 'neural networks (computer)', 'pancreatic neoplasms', 'pancreatitis' and 'pancreatic diseases'. A systematic review of the articles was performed. RESULTS: Eleven articles were identified, published between 1993 and 2007. The situations that lend themselves best to analysis by ANNs are complex multifactorial relationships, medical decisions when a second opinion is needed and when automated interpretation is required, for example in a situation of an inadequate number of experts. CONCLUSION: Conventional linear models have limitations in terms of diagnosis and prediction of outcome in acute pancreatitis and pancreatic cancer. Management of these disorders can be improved by applying ANNs to existing clinical parameters and newly established gene expression profiles.},
  author       = {Bartosch-Härlid, Anna and Andersson, Bodil and Aho, Ursula and Nilsson, J and Andersson, Roland},
  issn         = {1365-2168},
  language     = {eng},
  number       = {7},
  pages        = {817--826},
  publisher    = {John Wiley & Sons},
  series       = {British Journal of Surgery},
  title        = {Artificial neural networks in pancreatic disease.},
  url          = {http://dx.doi.org/10.1002/bjs.6239},
  volume       = {95},
  year         = {2008},
}