Identifying "preperimetric" glaucoma in standard automated perimetry visual fields.

PURPOSE To compare the visual fields (VFs) of preperimetric open angle glaucoma (OAG) patients (preperimetric glaucoma VFs, PPGVFs) with the VFs of healthy eyes, and to discriminate these two groups by using the Random Forests machine-learning method. METHODS All VFs before a first diagnosis of manifest glaucoma (Anderson-Patella's criteria) were classified as PPGVFs. Series of VFs were obtained with the Humphrey Field Analyzer 30-2 program from 171 PPGVFs from 53 eyes in 51 OAG or OAG suspect patients and 108 healthy eyes of 87 normal subjects. The area under the receiver operating characteristic curve (AROC) in discriminating between PPGVFs and healthy VFs was calculated by using the Random Forests method, with 52 total deviation (TD) values, mean deviation (MD), and pattern standard deviation (PSD) as predictors. RESULTS There was a significant difference in MD between healthy VFs and PPGVFs (-0.03 ± 1.11 and -0.91 ± 1.56 dB [mean ± standard deviation], respectively; P < 0.001, linear mixed model) and in PSD (1.56 ± 0.33 and 1.97 ± 0.43 dB, respectively; P < 0.001). A significant difference was observed in the TD values between healthy VFs and PPGVFs at 25 (P < 0.001) of 52 test points (linear mixed model). The AROC obtained by using the Random Forests method was 79.0% (95% confidence interval, 73.5%-84.5%). CONCLUSIONS Differences exist between healthy VFs and VFs of preperimetric glaucoma eyes, which go on to develop manifest glaucoma; these two groups of VFs could be well distinguished by using the Random Forests classifier.

[1]  Philip P. Chen,et al.  Prediction of Visual Field Defects on Standard Automated Perimetry by Screening C-20-1 Frequency Doubling Technology Perimetry , 2006, Journal of glaucoma.

[2]  A J Adams,et al.  Progression of early glaucomatous visual field loss as detected by blue-on-yellow and standard white-on-white automated perimetry. , 1993, Archives of ophthalmology.

[3]  B. Bengtsson,et al.  The effect of perimetric experience in patients with glaucoma. , 1996, Archives of ophthalmology.

[4]  S M Drance,et al.  The early field defects in glaucoma. , 1969, Investigative ophthalmology.

[5]  Pamela A. Sample,et al.  Specification of progression in glaucomatous visual field loss, applying locally condensed stimulus arrangements , 2009, Graefe's Archive for Clinical and Experimental Ophthalmology.

[6]  Y. Aoyama,et al.  A method to measure visual field sensitivity at the edges of glaucomatous scotomata. , 2014, Investigative ophthalmology & visual science.

[7]  J. Caprioli,et al.  Frequency doubling perimetry and short-wavelength automated perimetry to detect early glaucoma. , 2007, Ophthalmology.

[8]  F. Medeiros,et al.  Frequency doubling technology perimetry abnormalities as predictors of glaucomatous visual field loss. , 2004, American journal of ophthalmology.

[9]  J. Wild,et al.  The influence of the learning effect on automated perimetry in patients with suspected glaucoma , 1989, Acta ophthalmologica.

[10]  Robert N Weinreb,et al.  Comparison of different spectral domain OCT scanning protocols for diagnosing preperimetric glaucoma. , 2013, Investigative ophthalmology & visual science.

[11]  Makoto Aihara,et al.  Cross-sectional study: Does combining optical coherence tomography measurements using the ‘Random Forest’ decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects? , 2013, BMJ Open.

[12]  A. Tafreshi,et al.  Diagnosing preperimetric glaucoma with spectral domain optical coherence tomography. , 2012, Ophthalmology.

[13]  G. Dunkelberger,et al.  Retinal ganglion cell atrophy correlated with automated perimetry in human eyes with glaucoma. , 1989, American journal of ophthalmology.

[14]  Masanori Hangai,et al.  Frequency-doubling technology and retinal measurements with spectral-domain optical coherence tomography in preperimetric glaucoma , 2012, Graefe's Archive for Clinical and Experimental Ophthalmology.

[15]  A Heijl,et al.  Visual field interpretation with empiric probability maps. , 1989, Archives of ophthalmology.

[16]  J. Hardin,et al.  Generalized Linear Models and Extensions , 2001 .

[17]  Sang-Woo Park,et al.  Comparison of Scanning Laser Polarimetry and Optical Coherence Tomography in Preperimetric Glaucoma , 2011, Optometry and vision science : official publication of the American Academy of Optometry.

[18]  David Ward,et al.  Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data , 2003, Bioinform..

[19]  Ted Maddess,et al.  The influence of sampling errors on test-retest variability in perimetry. , 2011, Investigative Ophthalmology and Visual Science.

[20]  Hiroshi Murata,et al.  Combining multiple HRT parameters using the 'Random Forests' method improves the diagnostic accuracy of glaucoma in emmetropic and highly myopic eyes. , 2014, Investigative ophthalmology & visual science.

[21]  Ioannis N. Kouris,et al.  A comparative study of pattern recognition classifiers to predict physical activities using smartphones and wearable body sensors. , 2012, Technology and health care : official journal of the European Society for Engineering and Medicine.

[22]  C. Johnson,et al.  Blue-on-yellow perimetry can predict the development of glaucomatous visual field loss. , 1993, Archives of ophthalmology.

[23]  Douglas R. Anderson.  Automated Static Perimetry , 1992 .

[24]  A. Ferreras,et al.  Can Frequency-doubling Technology and Short-wavelength Automated Perimetries Detect Visual Field Defects Before Standard Automated Perimetry in Patients With Preperimetric Glaucoma? , 2007, Journal of glaucoma.

[25]  João Maroco,et al.  Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests , 2011, BMC Research Notes.

[26]  R S Harwerth,et al.  Ganglion cell losses underlying visual field defects from experimental glaucoma. , 1999, Investigative ophthalmology & visual science.

[27]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[28]  M. Bruce Shields,et al.  Textbook of glaucoma , 1987 .

[29]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[30]  Early Detection of Thinning of Retinal Nerve Fiber Layer in Glaucomatous Eyes by Optical Coherence Tomography 3000: Analysis of Retinal Nerve Fiber Layer Corresponding to the Preserved Hemivisual Field , 2005, Ophthalmic Research.

[31]  S. Graham,et al.  Detection of early visual field loss in glaucoma using frequency-doubling perimetry and short-wavelength automated perimetry. , 2003, Archives of ophthalmology.

[32]  Alan L. Yuille,et al.  Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief , 2011, NeuroImage.

[33]  P A Sample,et al.  Short-wavelength color visual fields in glaucoma suspects at risk. , 1993, American journal of ophthalmology.

[34]  Ilana Traynis,et al.  Prevalence and nature of early glaucomatous defects in the central 10° of the visual field. , 2014, JAMA ophthalmology.

[35]  Dong Myung Kim,et al.  Diagnostic Ability of Spectral-domain Versus Time-domain Optical Coherence Tomography in Preperimetric Glaucoma , 2014, Journal of glaucoma.

[36]  Achim Zeileis,et al.  BMC Bioinformatics BioMed Central Methodology article Conditional variable importance for random forests , 2008 .

[37]  Na Young Lee,et al.  Interpretation of the Humphrey Matrix 24-2 test in the diagnosis of preperimetric glaucoma , 2008, Japanese Journal of Ophthalmology.

[38]  M. Sung,et al.  Diagnostic Validity of Macular Ganglion Cell-Inner Plexiform Layer Thickness Deviation Map Algorithm Using Cirrus HD-OCT in Preperimetric and Early Glaucoma , 2014, Journal of glaucoma.

[39]  J. Weber,et al.  What is the most suitable grid for computer perimetry in glaucoma patients? , 1986, Ophthalmologica. Journal international d'ophtalmologie. International journal of ophthalmology. Zeitschrift fur Augenheilkunde.

[40]  Ramón Díaz-Uriarte,et al.  Gene selection and classification of microarray data using random forest , 2006, BMC Bioinformatics.

[41]  Mohak Shah,et al.  Evaluating Learning Algorithms: A Classification Perspective , 2011 .

[42]  S Tsukahara,et al.  Prediction of future scotoma on conventional automated static perimetry using frequency doubling technology perimetry , 2006, British Journal of Ophthalmology.

[43]  T. Zimmerman,et al.  Clinical Pathways in Glaucoma , 2001 .

[44]  W. Youden,et al.  Index for rating diagnostic tests , 1950, Cancer.

[45]  Preperimetric Glaucoma Assessment With Scanning Laser Polarimetry (GDx VCC): Analysis of Retinal Nerve Fiber Layer by Sectors , 2007, Journal of glaucoma.

[46]  J. Miller,et al.  The accuracy and clinical application of predictive models for primary open-angle glaucoma in ocular hypertensive individuals. , 2008, Ophthalmology.

[47]  Robert C. Glen,et al.  Random Forest Models To Predict Aqueous Solubility , 2007, J. Chem. Inf. Model..

[48]  F. Medeiros,et al.  Heidelberg Edge Perimetry for the Detection of Early Glaucomatous Damage: A Case Report , 2013, Case Reports in Ophthalmology.