PRECIS:Corvis ST Tonometry and Ocular Response Analyzer (ORA) measurements were conducted in primary open-angle glaucoma and normative subjects. Many parameters were significantly correlated, ...however, the strengths were weak to moderate.
PURPOSE:Reichert ORA parameters are derived from pressure information following the application of air-jet, whereas detailed structural observation can be made using the Corneal Visualization Scheimpflug Technology instrument (CST). The purpose of the study was to investigate the association between CST measurements and ORA measured corneal hysteresis (CH).
METHODS:Measurements of CST, ORA, axial length, average corneal curvature, central corneal thickness (CCT) and intraocular pressure with Goldmann applanation tonometry were carried out in 104 eyes of 104 patients with primary open-angle glaucoma and 35 eyes from normative subjects. The association between CST and ORA parameters was assessed using linear regression analysis, with model selection based on the second order bias corrected Akaike Information Criterion index.
RESULTS:Deformation amplitude ratio (corneal softness, R=−0.51), SP A1 (corneal stiffness, R=0.41), and Inverse Radius (integrated area under the curve of the inverse concave radius, R=−0.44) were significantly correlated with CH (P <0.05). The optimal model to explain CH using CST measurements was given byCH=−76.3+4.6×A1 time (applanation time in the corneal inward movement)+1.9×A2 time (second applanation time in the corneal outward movement) + 3.1 × highest concavity deformation amplitude (magnitude of movement of the corneal apex from before deformation to its highest concavity) + 0.016×CCT (R=0.67; P<0.001).
CONCLUSIONS:CST parameters are significant, but weakly or moderately, related to ORA measured CH.
The purpose of the study was to investigate the number of examinations required to precisely predict the future central 10-degree visual field (VF) test and to evaluate the effect of fitting ...non-linear models, including quadratic regression, exponential regression, logistic regression, and M-estimator robust regression model, for eyes with glaucoma. 180 eyes from 133 open angle glaucoma patients with a minimum of 13 Humphrey Field Analyzer 10-2 SITA standard VF tests were analyzed in this study. Using trend analysis with ordinary least squares linear regression (OLSLR), the first, second, and third future VFs were predicted in a point-wise (PW) manner using a varied number of prior VF sequences, and mean absolute errors (MAE) were calculated. The number of VFs needed to reach the minimum 95% confidence interval (CI) of the MAE of the OLSLR was investigated. We also examined the effect of applying other non-linear models. When predicting the first, second, and third future VFs using OLSLR, the minimum MAE was obtained using VF1-12 (2.15 ± 0.98 dB), VF1-11 (2.33 ± 1.10 dB), and VF1-10 (2.63 ± 1.36 dB), respectively. To reach the 95% CI of these MAEs, 10, 10, and 8 VFs were needed for the first, second and third future VF predictions, respectively. No improvement was observed by applying non-linear regression models. As a conclusion, approximately 8-10 VFs were needed to achieve an accurate prediction of PW VF sensitivity of the 10-degree central VF.
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We recently reported on the usefulness of retinal artery trajectory in estimating the magnitude of retinal stretch due to myopia. The purpose of the present study was to elucidate the relationship ...between the peripapillary retinal artery angle (PRAA) and thickness of the macular ganglion cell-inner plexiform layer (GCIPL).
This r included 138 healthy eyes of 79 subjects older than 20 years of age without any known eye disease. GCIPL thickness was separated into eight sectors according to quadrant and eccentricity from the fovea. The PRAA was calculated as the angle between the superior and inferior retinal arteries. Relationships between whole GCIPL thickness (average and sectorial) and the values of PRAA and axial length (AL) were investigated using a linear mixed model.
Average GCIPL thickness in the whole scanned area decreased significantly with narrowing of the PRAA with and without adjusting for AL. Sectorized macular GCIPL thickness also decreased significantly, with narrowing of the PRAA in seven out of the eight with the adjustment of AL, the exception being the inferior peripheral temporal sector.
Macular GCIPL thickness decreased significantly with narrowing of the PRAA on average and in seven out of eight sectors.
To investigate the structure-function mapping in the central 10° by relating Humphrey field analyzer (HFA) 10-2 visual field (VF) and circumpapillary retinal nerve fiber layer (cpRNFL) thickness from ...spectral-domain optical coherence tomography (SD-OCT). We also compared the obtained results with a previously reported mapping between 10-2 VF and the optic disc.
In 151 eyes of 151 POAG patients and 35 eyes from 35 healthy participants, cpRNFL thickness measurements were obtained using SD-OCT and the 10-2 VF was measured with the HFA. The relationship between visual sensitivity and cpRNL thickness values in the temporal 180° was analyzed using least absolute shrinkage and selection operator (LASSO) regression. The optic disc angle corresponding to each VF test point was then derived using the coefficients from the optimal LASSO regression.
The structure-function map obtained was largely consistent with the mapping reported previously; superior central VF test points correspond to a more vulnerable area of the optic disc, more distant toward the inferior pole from the center of the temporal quadrant (9:00 o'clock for the right eye) while inferior VF test points correspond closer to the center of the temporal quadrant. The prediction error tended to be large in the 'more vulnerable area' in the map reported previously.
The structure-function map obtained largely confirms the previously reported map; however, some important differences were observed.
To validate the prediction accuracy of variational Bayes linear regression (VBLR) with two datasets external to the training dataset.
The training dataset consisted of 7268 eyes of 4278 subjects from ...the University of Tokyo Hospital. The Japanese Archive of Multicentral Databases in Glaucoma (JAMDIG) dataset consisted of 271 eyes of 177 patients, and the Diagnostic Innovations in Glaucoma Study (DIGS) dataset includes 248 eyes of 173 patients, which were used for validation. Prediction accuracy was compared between the VBLR and ordinary least squared linear regression (OLSLR). First, OLSLR and VBLR were carried out using total deviation (TD) values at each of the 52 test points from the second to fourth visual fields (VFs) (VF2-4) to 2nd to 10th VF (VF2-10) of each patient in JAMDIG and DIGS datasets, and the TD values of the 11th VF test were predicted every time. The predictive accuracy of each method was compared through the root mean squared error (RMSE) statistic.
OLSLR RMSEs with the JAMDIG and DIGS datasets were between 31 and 4.3 dB, and between 19.5 and 3.9 dB. On the other hand, VBLR RMSEs with JAMDIG and DIGS datasets were between 5.0 and 3.7, and between 4.6 and 3.6 dB. There was statistically significant difference between VBLR and OLSLR for both datasets at every series (VF2-4 to VF2-10) (P < 0.01 for all tests). However, there was no statistically significant difference in VBLR RMSEs between JAMDIG and DIGS datasets at any series of VFs (VF2-2 to VF2-10) (P > 0.05).
VBLR outperformed OLSLR to predict future VF progression, and the VBLR has a potential to be a helpful tool at clinical settings.
To assess the thickness of the photoreceptor layer in the macular region in glaucomatous eyes.
Humphrey 10-2 visual field (VF) testing was carried out and mean threshold (mTH) was calculated in 118 ...eyes from 118 patients with open angle glaucoma. Macular optical coherence tomography (OCT) measurements (RS 3000, Nidek Co.ltd., Aichi, Japan) were also carried out in all eyes. Thickness measurements were recorded in the outer segment and retinal pigment epithelium (OS+RPE), the nerve fiber layer (NFL), the ganglion cell layer and inner plexiform layer (GCL+IPL), the inner nuclear layer and outer plexiform layer (INL+OPL) and the outer nuclear layer and inner segment (ONL+IS). The relationship between mTH and the thickness of these five different layers was investigated. Additionally, the influence of OS+RPE on mTH was investigated using partial correlation eliminating the effect of other variables of NFL, GCL+IPL, INL+OPL, ONL+IS, age, gender and axial length.
The thickness of the OS+RPE layer was significantly decreased with the decrease of mTH (coefficient = 0.63 p <0.001). Partial correlation analysis suggested OS+RPE thickness is significantly (coefficient = 0.31, p <0.001) related to mTH, independent from NFL, GCL+IPL, INL+OPL, ONL+IS, age, gender and axial length.
The thickness of the RPE+OS layer appears to be related to visual sensitivity in glaucoma.
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The aim was to establish and evaluate a new clustering method for visual field (VF) test points to predict future VF in retinitis pigmentosa. A Humphrey Field Analyzer 10-2 test was clustered using ...total deviation values from 858 VFs. We stratified 68 test points into 24 sectors. Then, mean absolute error (MAE) of the sector-wise regression with them (S1) was evaluated using 196 eyes with 10 VF sequences and compared to pointwise linear regression (PLR), mean sensitivity of total area (MS) and also another sector-wise regression basing on VF mapping for glaucoma (29 sectors; S2). MAE with S1 were smaller than with PLR when between the first-third and first-seventh VFs were used. MAE with the method were significantly smaller than those of S2 when between the first-sixth and first-ninth VFs were used. The MAE of MS was smaller than those with S1 only when first to 3rd and first to 4th VFs were used; however, the prediction accuracy became far larger than any other methods when larger number of VFs were used. More accurate prediction was achieved using this new sector-wise regression than with PLR. In addition, the obtained cluster was more useful than that for glaucoma to predict progression.
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PURPOSE OF THE STUDY:We recently reported that it is beneficial to apply least absolute shrinkage and selection operator (Lasso) regression to predict future 24-2 visual field (VF) progression. The ...purpose of the current study was to investigate the usefulness of Lasso regression to predict VF progression in the central 10 degrees (10-2) in glaucoma patients.
METHODS:Series of 10 VFs (Humphrey Field Analyzer 10-2 SITA-standard) from each of 149 eyes in 110 open angle glaucoma patients, obtained over 5.7±1.4 years (mean±SD) were investigated. Mean deviation values of the 10th VF were predicted using varying numbers of VFs (ranging from the first to third VFs to the first to ninth VFs), applying ordinary least square regression (OLSLR) and Lasso regression. Absolute prediction errors were then compared.
RESULTS:With OLSLR, prediction error varied between 5.4±5.0 (using first to third VFs) and 1.1±1.6 dB (using first to ninth VFs). Significantly smaller prediction errors were obtained with Lasso regression, in particular with small numbers of VFs (from 2.1±2.8first to third VFs, to 1.0±1.6 dBfirst to ninth VFs). A large λ value, which is an index showing the degree of penalty in Lasso regression, was observed when a small number of VFs were used for prediction.
CONCLUSION:Mean deviation prediction using OLSLR with a small number of VFs resulted in large prediction errors. It was useful to apply Lasso regression when predicting future progression of the central 10 degrees, compared to OLSLR.
The purpose of the current study was to predict intraocular pressure (IOP) using color fundus photography with a deep learning (DL) model, or, systemic variables with a multivariate linear regression ...model (MLM), along with least absolute shrinkage and selection operator regression (LASSO), support vector machine (SVM), and Random Forest: (RF). Training dataset included 3883 examinations from 3883 eyes of 1945 subjects and testing dataset 289 examinations from 289 eyes from 146 subjects. With the training dataset, MLM was constructed to predict IOP using 35 systemic variables and 25 blood measurements. A DL model was developed to predict IOP from color fundus photographs. The prediction accuracy of each model was evaluated through the absolute error and the marginal R-squared (mR
), using the testing dataset. The mean absolute error with MLM was 2.29 mmHg, which was significantly smaller than that with DL (2.70 dB). The mR
with MLM was 0.15, whereas that with DL was 0.0066. The mean absolute error (between 2.24 and 2.30 mmHg) and mR
(between 0.11 and 0.15) with LASSO, SVM and RF were similar to or poorer than MLM. A DL model to predict IOP using color fundus photography proved far less accurate than MLM using systemic variables.
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