Linked Life Data

10909929

Source:http://linkedlifedata.com/resource/pubmed/id/10909929

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2000-11-24
pubmed:abstractText
For an objective interpretation of cerebral metabolic pattern to find epileptogenic zones in patients with temporal lobe epilepsy (TLE), we developed a computer-aided classifier using an artificial neural network (ANN). We studied 261 epilepsy patients diagnosed as no abnormal findings (NA, n = 64), left TLE (n = 116), or right TLE (n = 81) on interictal brain F-18-fluorodeoxyglucose positron emission tomography (FDG PET) by the consensus of two expert physicians. Seventeen asymmetry indexes between the mean counts of the 34 mirrored regions were extracted from the spatially normalized images and used as input parameters. The three diagnoses of NA, left TLE, and right TLE were used as outputs of the ANN. The structure of the ANN was optimized with variable error goals and the number of hidden units. On the criteria of agreement of diagnoses with those of expert viewers, the best classifier was chosen, which yielded a maximum average agreement of 85% for the test set when we used an error goal of 20 (sum of squared error) and ten hidden units. We could devise an ANN that performed as well in diagnosing left or right TLE on FDG PET as human experts and could be used as a clinical decision support tool.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0278-0062
pubmed:author
pubmed:issnType
Print
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
347-55
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Localization of epileptogenic zones in F-18 FDG brain PET of patients with temporal lobe epilepsy using artificial neural network.
pubmed:affiliation
Interdisciplinary Program in Medical and Biological Engineering Major, Seoul National University, Korea.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't