Ryo Asaoka1, Aiko Iwase2, Kazunori Hirasawa3, Hiroshi Murata1, Makoto Araie4. 1. Department of Ophthalmology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan. 2. Tajimi Iwase Eye Clinic, Tajimi, Japan. 3. Department of Ophthalmology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan Department of Ophthalmology, Graduate School of Medical Science, Kitasato University, Kanagawa, Japan. 4. Kanto Central Hospital of the Mutual Aid Association of Public School Teachers, Tokyo, Japan.
Abstract
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. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.
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. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.
Entities:
Keywords:
Random Forests method; glaucoma; preperimetric stage; visual field
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