Linked Life Data

8070077

Source:http://linkedlifedata.com/resource/pubmed/id/8070077

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
9
pubmed:dateCreated
1994-9-29
pubmed:abstractText
Infrared (IR) spectroscopy is used to analyze urinary calculus (renal stone) constituents. However, interpretation of IR spectra for quantifying urinary calculus constituents in mixtures is difficult, requiring expert knowledge by trained technicians. In our laboratory IR spectra of unknown calculi are compared with references spectra in a computerized library search of 235 reference spectra from various mixtures of constituents in different proportions, followed by visual interpretation of band intensities for more precise semiquantitative determination of the composition. To minimize the need for this last step, we tested artificial neural network models for detecting the most frequently occurring compositions of urinary calculi. Using constrained mixture designs, we prepared various samples containing ammonium hydrogen urate, brushite, carbonate apatite, cystine, struvite, uric acid, weddellite, and whewellite for use as a training set. We assayed known artificial mixtures as well as selected patients' samples from which the semiquantitative compositions were determined by computerized library search followed by visual interpretation. Neural network analysis was more accurate than the library search and required less expert knowledge because careful visual inspection of the band intensities could be omitted. We conclude that neural networks are promising tools for routine quantification of urinary calculus compositions and for other related types of analyses in the clinical laboratory.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
0009-9147
pubmed:author
pubmed:issnType
Print
pubmed:volume
40
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1692-7
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
1994
pubmed:articleTitle
Artificial neural network predictions of urinary calculus compositions analyzed with infrared spectroscopy.
pubmed:affiliation
Central Laboratory for Clinical Chemistry, University Hospital Groningen, The Netherlands.
pubmed:publicationType
Journal Article