Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT
Joint Authors
Silva, Fabrício Reis
Gonçalves Vidotti, Vanessa
Gomi, Edson Satoshi
Barella, Kleyton Arlindo
Costa, Vital Paulino
Dias, Marcelo
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-28
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Barella, Kleyton Arlindo& Costa, Vital Paulino& Gonçalves Vidotti, Vanessa& Silva, Fabrício Reis& Dias, Marcelo& Gomi, Edson Satoshi. 2013. Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT. Journal of Ophthalmology،Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-498150
Modern Language Association (MLA)
Barella, Kleyton Arlindo…[et al.]. Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT. Journal of Ophthalmology No. 2013 (2013), pp.1-7.
https://search.emarefa.net/detail/BIM-498150
American Medical Association (AMA)
Barella, Kleyton Arlindo& Costa, Vital Paulino& Gonçalves Vidotti, Vanessa& Silva, Fabrício Reis& Dias, Marcelo& Gomi, Edson Satoshi. Glaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT. Journal of Ophthalmology. 2013. Vol. 2013, no. 2013, pp.1-7.
https://search.emarefa.net/detail/BIM-498150
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-498150