{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T08:44:45Z","timestamp":1790671485718,"version":"4.1.0"},"reference-count":23,"publisher":"Wiley","license":[{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["07\/51281-9"],"award-info":[{"award-number":["07\/51281-9"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Ophthalmology"],"published-print":{"date-parts":[[2013]]},"abstract":"<jats:p>\n                    <jats:italic>Purpose<\/jats:italic>\n                    . To investigate the diagnostic accuracy of machine learning classifiers (MLCs) using retinal nerve fiber layer (RNFL) and optic nerve (ON) parameters obtained with spectral domain optical coherence tomography (SD-OCT).\n                    <jats:italic>Methods<\/jats:italic>\n                    . Fifty-seven patients with early to moderate primary open angle glaucoma and 46 healthy patients were recruited. All 103 patients underwent a complete ophthalmological examination, achromatic standard automated perimetry, and imaging with SD-OCT. Receiver operating characteristic (ROC) curves were built for RNFL and ON parameters. Ten MLCs were tested. Areas under ROC curves (aROCs) obtained for each SD-OCT parameter and MLC were compared.\n                    <jats:italic>Results<\/jats:italic>\n                    . The mean age was\n                    <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                      <mml:mn fontstyle=\"italic\">56.5<\/mml:mn>\n                      <mml:mo>\u00b1<\/mml:mo>\n                      <mml:mn fontstyle=\"italic\">8.9<\/mml:mn>\n                    <\/mml:math>\n                    years for healthy individuals and\n                    <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\">\n                      <mml:mn fontstyle=\"italic\">59.9<\/mml:mn>\n                      <mml:mo>\u00b1<\/mml:mo>\n                      <mml:mn fontstyle=\"italic\">9.0<\/mml:mn>\n                    <\/mml:math>\n                    years for glaucoma patients (\n                    <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\">\n                      <mml:mi>P<\/mml:mi>\n                      <mml:mo>=<\/mml:mo>\n                      <mml:mn fontstyle=\"italic\">0.054<\/mml:mn>\n                    <\/mml:math>\n                    ). Mean deviation values were \u22121.4\u2009dB for healthy individuals and \u22124.0\u2009dB for glaucoma patients (\n                    <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\">\n                      <mml:mi>P<\/mml:mi>\n                      <mml:mo>&lt;<\/mml:mo>\n                      <mml:mn fontstyle=\"italic\">0.001<\/mml:mn>\n                    <\/mml:math>\n                    ). SD-OCT parameters with the greatest aROCs were cup\/disc area ratio (0.846) and average cup\/disc (0.843). aROCs obtained with classifiers varied from 0.687 (CTREE) to 0.877 (RAN). The aROC obtained with RAN (0.877) was not significantly different from the aROC obtained with the best single SD-OCT parameter (0.846) (\n                    <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\">\n                      <mml:mi>P<\/mml:mi>\n                      <mml:mo>=<\/mml:mo>\n                      <mml:mn fontstyle=\"italic\">0.542<\/mml:mn>\n                    <\/mml:math>\n                    ).\n                    <jats:italic>Conclusion<\/jats:italic>\n                    . MLCs showed good accuracy but did not improve the sensitivity and specificity of SD-OCT for the diagnosis of glaucoma.\n                  <\/jats:p>","DOI":"10.1155\/2013\/789129","type":"journal-article","created":{"date-parts":[[2013,11,28]],"date-time":"2013-11-28T16:02:09Z","timestamp":1385654529000},"page":"1-7","source":"Crossref","is-referenced-by-count":47,"title":["Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT"],"prefix":"10.1155","volume":"2013","author":[{"given":"Kleyton Arlindo","family":"Barella","sequence":"first","affiliation":[{"name":"Faculty of Medical Sciences, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vital Paulino","family":"Costa","sequence":"additional","affiliation":[{"name":"Faculty of Medical Sciences, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"Gon\u00e7alves Vidotti","sequence":"additional","affiliation":[{"name":"Faculty of Medical Sciences, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabr\u00edcio Reis","family":"Silva","sequence":"additional","affiliation":[{"name":"Faculty of Medical Sciences, Universidade Estadual de Campinas (UNICAMP), Campinas, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo","family":"Dias","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of S\u00e3o Paulo (USP), S\u00e3o Paulo, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edson Satoshi","family":"Gomi","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of S\u00e3o Paulo (USP), S\u00e3o Paulo, SP, 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