Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/88825
Type: Artigo de periódico
Title: Glaucoma Diagnostic Accuracy Of Machine Learning Classifiers Using Retinal Nerve Fiber Layer And Optic Nerve Data From Sd-oct
Author: Barella K.A.
Costa V.P.
Goncalves Vidotti V.
Silva F.R.
Dias M.
Gomi E.S.
Abstract: Purpose. 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). Methods. 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. Results. The mean age was 56.5 ± 8.9 years for healthy individuals and 59.9 ± 9.0 years for glaucoma patients (P = 0.054). Mean deviation values were -1.4 dB for healthy individuals and -4.0 dB for glaucoma patients (P < 0.001). 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) (P = 0.542). Conclusion. MLCs showed good accuracy but did not improve the sensitivity and specificity of SD-OCT for the diagnosis of glaucoma. © 2013 Kleyton Arlindo Barella et al.
Editor: 
Rights: aberto
Identifier DOI: 10.1155/2013/789129
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-84893842965&partnerID=40&md5=ae37512421b2cca020e2a9abe336f46e
Date Issue: 2013
Appears in Collections:Unicamp - Artigos e Outros Documentos

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