Determination of the composition of human urinary calculi composed of whewellite, weddellite and carbonate apatite using artificial neural networks
More than half of the analyzed calculi from patients from Macedonia are composed of whewellite, weddellite and carbonate apatite (as single components or in binary or ternary mixtures). In order to develop a simple and satisfactorily reliable method for quantitative analysis of urinary calculi, the...
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| Veröffentlicht in: | Analytica chimica acta 2003-09, Vol.491 (2), p.211-218 |
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| container_title | Analytica chimica acta |
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| creator | Kuzmanovski, Igor Trpkovska, Mira Šoptrajanov, Bojan Stefov, Viktor |
| description | More than half of the analyzed calculi from patients from Macedonia are composed of whewellite, weddellite and carbonate apatite (as single components or in binary or ternary mixtures). In order to develop a simple and satisfactorily reliable method for quantitative analysis of urinary calculi, the possibility was explored to employ artificial neural networks (ANNs) as a tool for such a purpose. By changing the number of input and hidden neurons, a search was made for the three-layered feed-forward ANN which would give the best performance. The root-mean-square errors (RMSE) for the test samples are: 0.035 for whewellite, 0.064 for weddellite and 0.078 for carbonate apatite. The accuracy of the method was checked using standard-addition method on real samples. The discrepancies between calculated and predicted mass fraction of constituents were in the range acceptable for use of the proposed method in clinical laboratories. |
| doi_str_mv | 10.1016/S0003-2670(03)00787-6 |
| format | Article |
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In order to develop a simple and satisfactorily reliable method for quantitative analysis of urinary calculi, the possibility was explored to employ artificial neural networks (ANNs) as a tool for such a purpose. By changing the number of input and hidden neurons, a search was made for the three-layered feed-forward ANN which would give the best performance. The root-mean-square errors (RMSE) for the test samples are: 0.035 for whewellite, 0.064 for weddellite and 0.078 for carbonate apatite. The accuracy of the method was checked using standard-addition method on real samples. The discrepancies between calculated and predicted mass fraction of constituents were in the range acceptable for use of the proposed method in clinical laboratories.</description><identifier>ISSN: 0003-2670</identifier><identifier>EISSN: 1873-4324</identifier><identifier>DOI: 10.1016/S0003-2670(03)00787-6</identifier><identifier>CODEN: ACACAM</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Analytical chemistry ; Applied sciences ; Artificial intelligence ; Artificial neural network ; Biological and medical sciences ; Carbonate apatite ; Chemistry ; Computer science; control theory; systems ; Connectionism. Neural networks ; Exact sciences and technology ; Infrared spectroscopy ; Medical sciences ; Nephrology. Urinary tract diseases ; Spectrometric and optical methods ; Urinary calculi ; Urinary lithiasis ; Weddellite ; Whewellite</subject><ispartof>Analytica chimica acta, 2003-09, Vol.491 (2), p.211-218</ispartof><rights>2003 Elsevier B.V.</rights><rights>2003 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c453t-c9248611820e19012723230775965c0eae333a36c720e822db0b81b3f9dda0ff3</citedby><cites>FETCH-LOGICAL-c453t-c9248611820e19012723230775965c0eae333a36c720e822db0b81b3f9dda0ff3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/S0003-2670(03)00787-6$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,777,781,3537,27905,27906,45976</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=15057835$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Kuzmanovski, Igor</creatorcontrib><creatorcontrib>Trpkovska, Mira</creatorcontrib><creatorcontrib>Šoptrajanov, Bojan</creatorcontrib><creatorcontrib>Stefov, Viktor</creatorcontrib><title>Determination of the composition of human urinary calculi composed of whewellite, weddellite and carbonate apatite using artificial neural networks</title><title>Analytica chimica acta</title><description>More than half of the analyzed calculi from patients from Macedonia are composed of whewellite, weddellite and carbonate apatite (as single components or in binary or ternary mixtures). In order to develop a simple and satisfactorily reliable method for quantitative analysis of urinary calculi, the possibility was explored to employ artificial neural networks (ANNs) as a tool for such a purpose. By changing the number of input and hidden neurons, a search was made for the three-layered feed-forward ANN which would give the best performance. The root-mean-square errors (RMSE) for the test samples are: 0.035 for whewellite, 0.064 for weddellite and 0.078 for carbonate apatite. The accuracy of the method was checked using standard-addition method on real samples. The discrepancies between calculated and predicted mass fraction of constituents were in the range acceptable for use of the proposed method in clinical laboratories.</description><subject>Analytical chemistry</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Artificial neural network</subject><subject>Biological and medical sciences</subject><subject>Carbonate apatite</subject><subject>Chemistry</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Exact sciences and technology</subject><subject>Infrared spectroscopy</subject><subject>Medical sciences</subject><subject>Nephrology. Urinary tract diseases</subject><subject>Spectrometric and optical methods</subject><subject>Urinary calculi</subject><subject>Urinary lithiasis</subject><subject>Weddellite</subject><subject>Whewellite</subject><issn>0003-2670</issn><issn>1873-4324</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><recordid>eNqFkcuOFCEUhonRxLb1EUzYaMZkyjlAVUGvzGQcL8kks1DXhIZTNloFLVB2fI55YemLunNWP4d855L_J-Q5g9cMWH_xCQBEw3sJZyBeAUglm_4BWTAlRdMK3j4ki7_IY_Ik52-15AzaBbl7iwXT5IMpPgYaB1o2SG2ctjH7P1-beTKBzqlS6Re1ZrTz6E8Quj2x2-AOx9EXPKc7dO74pia4iqd1rONrta1Lqs7Zh6_UpOIHb70ZacA5HaTsYvqen5JHgxkzPjvpknx5d_356kNzc_v-49XlTWPbTpTGrniresYUB2QrYFxywQVI2a36zgIaFEIY0VtZAcW5W8NasbUYVs4ZGAaxJGfHudsUf8yYi558tvV0EzDOWVf_OtUpaKGiL_-Pyr6urWctSXcEbYo5Jxz0Nvmp2qYZ6H1a-pCW3kehqx7S0n3te3FaYHI1eEgmWJ__NXfQSSX2898cOazG_PSYdLYeg0XnE9qiXfT3bPoNbSCrYQ</recordid><startdate>20030901</startdate><enddate>20030901</enddate><creator>Kuzmanovski, Igor</creator><creator>Trpkovska, Mira</creator><creator>Šoptrajanov, Bojan</creator><creator>Stefov, Viktor</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20030901</creationdate><title>Determination of the composition of human urinary calculi composed of whewellite, weddellite and carbonate apatite using artificial neural networks</title><author>Kuzmanovski, Igor ; Trpkovska, Mira ; Šoptrajanov, Bojan ; Stefov, Viktor</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c453t-c9248611820e19012723230775965c0eae333a36c720e822db0b81b3f9dda0ff3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Analytical chemistry</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Artificial neural network</topic><topic>Biological and medical sciences</topic><topic>Carbonate apatite</topic><topic>Chemistry</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>Exact sciences and technology</topic><topic>Infrared spectroscopy</topic><topic>Medical sciences</topic><topic>Nephrology. Urinary tract diseases</topic><topic>Spectrometric and optical methods</topic><topic>Urinary calculi</topic><topic>Urinary lithiasis</topic><topic>Weddellite</topic><topic>Whewellite</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kuzmanovski, Igor</creatorcontrib><creatorcontrib>Trpkovska, Mira</creatorcontrib><creatorcontrib>Šoptrajanov, Bojan</creatorcontrib><creatorcontrib>Stefov, Viktor</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Analytica chimica acta</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kuzmanovski, Igor</au><au>Trpkovska, Mira</au><au>Šoptrajanov, Bojan</au><au>Stefov, Viktor</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Determination of the composition of human urinary calculi composed of whewellite, weddellite and carbonate apatite using artificial neural networks</atitle><jtitle>Analytica chimica acta</jtitle><date>2003-09-01</date><risdate>2003</risdate><volume>491</volume><issue>2</issue><spage>211</spage><epage>218</epage><pages>211-218</pages><issn>0003-2670</issn><eissn>1873-4324</eissn><coden>ACACAM</coden><abstract>More than half of the analyzed calculi from patients from Macedonia are composed of whewellite, weddellite and carbonate apatite (as single components or in binary or ternary mixtures). In order to develop a simple and satisfactorily reliable method for quantitative analysis of urinary calculi, the possibility was explored to employ artificial neural networks (ANNs) as a tool for such a purpose. By changing the number of input and hidden neurons, a search was made for the three-layered feed-forward ANN which would give the best performance. The root-mean-square errors (RMSE) for the test samples are: 0.035 for whewellite, 0.064 for weddellite and 0.078 for carbonate apatite. The accuracy of the method was checked using standard-addition method on real samples. The discrepancies between calculated and predicted mass fraction of constituents were in the range acceptable for use of the proposed method in clinical laboratories.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/S0003-2670(03)00787-6</doi><tpages>8</tpages></addata></record> |
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| ispartof | Analytica chimica acta, 2003-09, Vol.491 (2), p.211-218 |
| issn | 0003-2670 1873-4324 |
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| source | Elsevier ScienceDirect Journals |
| subjects | Analytical chemistry Applied sciences Artificial intelligence Artificial neural network Biological and medical sciences Carbonate apatite Chemistry Computer science control theory systems Connectionism. Neural networks Exact sciences and technology Infrared spectroscopy Medical sciences Nephrology. Urinary tract diseases Spectrometric and optical methods Urinary calculi Urinary lithiasis Weddellite Whewellite |
| title | Determination of the composition of human urinary calculi composed of whewellite, weddellite and carbonate apatite using artificial neural networks |
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