Artificial neural networks in pancreatic disease.
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.


CONCLUSIONS
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.
Department of Cell and Organism Biology, Lund University, Lund, Sweden.
A
A
Bartosch-Härlid
B
B
Andersson
U
U
Aho
J
J
Nilsson
R
R
Andersson
D000208:Acute Disease; D003657:Decision Making; D042241:Early Diagnosis; D006801:Humans; D016571:Neural Networks, Computer; D010190:Pancreatic Neoplasms / Q000175:diagnosis*; D010195:Pancreatitis / Q000175:diagnosis*; D011379:Prognosis