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Purpose: To diagnose glaucoma based on spectral domain optical coherence tomography (SD-OCT) measurements using the ‘Random Forests’ method. Methods: SD-OCT was conducted in 126 eyes of 126 open angle glaucoma (OAG) patients and 84 eyes of 84 normal subjects. The Random Forests method was then applied to discriminate between glaucoma and normal eyes using 151 OCT parameters including thickness measurements of circumpapillary retinal nerve fiber layer (cpRNFL), the macular RNFL (mRNFL) and the ganglion cell layer-inner plexiform layer combined (GCIPL). The area under the receiver operating characteristic curve (AROC) was calculated using the Random Forests method adopting leave-one-out cross validation. For comparison, AROCs were calculated based on each one of the 151 OCT parameters. Results: The AROC obtained with the Random Forests method was 98.5% [95% Confidence interval (CI): 97.1–99.9%], which was significantly larger than the AROCs derived from any single OCT parameter (maxima were: 92.8 [CI: 89.4–96.2] %, 94.3 [CI: 91.1–97.6] % and 91.8 [CI: 88.2–95.4] % for cpRNFL-, mRNFL- and GCIPL-related parameters, respectively; P<0.05, DeLong’s method with Holm’s correction for multiple comparisons). The partial AROC above specificity of 80%, for the Random Forests method was equal to 18.5 [CI: 16.8–19.6] %, which was also significantly larger than the AROCs of any single OCT parameter (P<0.05, Bootstrap method with Holm’s correction for multiple comparisons). Conclusions: The Random Forests method, analyzing multiple SD-OCT parameters concurrently, significantly improves the diagnosis of glaucoma compared with using any single SD-OCT measurement. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to create a vision-related quality of life (VRQoL) prediction system to identify visual field (VF) test points associated with decreased VRQoL in patients with glaucoma. Method: VRQoL score was surveyed in 164 patients with glaucoma using the ‘Sumi questionnaire’. A binocular VF was created from monocular VFs by using the integrated VF (IVF) method. VRQoL score was predicted using the ‘Random Forest’ method, based on visual acuity (VA) of better and worse eyes (better-eye and worse-eye VA) and total deviation (TD) values from the IVF. For comparison, VRQoL scores were regressed (linear regression) against: (i) mean of TD (IVF MD); (ii) better-eye VA; (iii) worse-eye VA; and (iv) IVF MD and better- and worse-eye VAs. The rank of importance of IVF test points was identified using the Random Forest method. Results: The root mean of squared prediction error associated with the Random Forest method (0.30 to 1.97) was significantly smaller than those with linear regression models (0.34 to 3.38, p<0.05, ten-fold cross validation test). Worse-eye VA was the most important variable in all VRQoL tasks. In general, important VF test points were concentrated along the horizontal meridian. Particular areas of the IVF were important for different tasks: peripheral superior and inferior areas in the left hemifield for the ‘letters and sentences’ task, peripheral, mid-peripheral and para-central inferior regions for the ‘walking’ task, the peripheral superior region for the ‘going out’ task, and a broad scattered area across the IVF for the ‘dining’ task. Conclusion: The VRQoL prediction model with the Random Forest method enables clinicians to better understand patients’ VRQoL based on standard clinical measurements of VA and VF. [ABSTRACT FROM AUTHOR]

Purpose:To compare the performance of newly proposed point-wise linear regression (PLR) with the binomial test (binomial PLR) against mean deviation (MD) trend analysis and permutation analyses of PLR (PoPLR), in detecting global visual field (VF) progression in glaucoma. Methods:15 VFs (Humphrey Field Analyzer, SITA standard, 24-2) were collected from 96 eyes of 59 open angle glaucoma patients (6.0 ± 1.5 [mean ± standard deviation] years). Using the total deviation of each point on the 2nd to 16th VFs (VF2-16), linear regression analysis was carried out. The numbers of VF test points with a significant trend at various probability levels (p<0.025, 0.05, 0.075 and 0.1) were investigated with the binomial test (one-side). A VF series was defined as “significant” if the median p-value from the binomial test was <0.025. Similarly, the progression analysis was carried out using only second to sixth VFs (VF2-6). The performance of each method was evaluated using the ‘consistency measures’; proportion both significant (PBS): both VF series (VF2-6 and VF2-16) were “significant”, proportion both were not significant (PBNS): both were “not significant”, proportion inconsistently significant (PIS): VF2-16 was “not significant” but VF2-6 was “significant”. A similar analysis was carried out using VF2-7 and VF2-15 series, and the performance was compared with MD trend analysis and PoPLR. Results:The PBS of the binomial PLR method (0.14 to 0.86) was significantly higher than MD trend analysis (0.04 to 0.89) and PoPLR (0.09 to 0.93). The PIS of the proposed method (0.0 to 0.17) was significantly lower than the MD approach (0.0 to 0.67) and PoPLR (0.07 to 0.33). The PBNS of the three approaches were not significantly different. Conclusions:The binomial BLR method gives more consistent results than MD trend analysis and PoPLR, hence it will be helpful as a tool to ‘flag’ possible VF deterioration. [ABSTRACT FROM AUTHOR]