Document Detail


Neural net applied to anthropological material: a methodical study on the human nasal skeleton.
MedLine Citation:
PMID:  16130825     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
A new information processing method, an artificial neural net, was applied to characterise the variability of anthropological features of the human nasal skeleton. The aim was to find different types of nasal skeletons. A neural net with 15*15 nodes was trained by 17 standard anthropological parameters taken from 184 skulls of the Aachen collection. The trained neural net delivers its classification in a two-dimensional map. Different types of noses were locally separated within the map. Rare and frequent types may be distinguished after one passage of the complete collection through the net. Statistical descriptive analysis, hierarchical cluster analysis, and discriminant analysis were applied to the same data set. These parallel applications allowed comparison of the new approach to the more traditional ones. In general the classification by the neural net is in correspondence with cluster analysis and discriminant analysis. However, it goes beyond these classifications because of the possibility of differentiating the types in multi-dimensional dependencies. Furthermore, places in the map are kept blank for intermediate forms, which may be theoretically expected, but were not included in the training set. In conclusion, the application of a neural network is a suitable method for investigating large collections of biological material. The gained classification may be helpful in anatomy and anthropology as well as in forensic medicine. It may be used to characterise the peculiarity of a whole set as well as to find particular cases within the set.
Authors:
Andreas Prescher; Anne Meyers; Diedrich Gerf von Keyserlingk
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft     Volume:  187     ISSN:  0940-9602     ISO Abbreviation:  Ann. Anat.     Publication Date:  2005 Jul 
Date Detail:
Created Date:  2005-08-31     Completed Date:  2005-10-05     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100963897     Medline TA:  Ann Anat     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  261-9     Citation Subset:  IM    
Affiliation:
Institut für Anatomie der RWTH Aachen, Wendlingweg 2, Aachen D-52074, Germany. aprescher@ukaachen.de
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MeSH Terms
Descriptor/Qualifier:
Adult
Aged
Aged, 80 and over
Aging
Anthropology, Physical / methods
Bone Development
Cadaver
Europe
European Continental Ancestry Group
Female
Humans
Male
Middle Aged
Nasal Bone / anatomy & histology*
Neural Networks (Computer)
Skull / anatomy & histology*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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