Over the past few years, high-speed optical modulation technology operating in the quasi-millimeter-wave and millimeter-wave bands has garnered unprecedented interest owing to demands in new wireless ...applications such as 5G/beyond-5G mobile front-haul, high-resolution radars, antenna measurements, and imaging systems for security. The author has been attempting to develop high-speed electro-optic modulators operating in these bands utilizing antenna-coupled electrodes and stacked-substrate structures. In this article, several high-speed optical modulators for converting wireless signals with advanced functions are presented and discussed. The results of this study can guide ongoing research in the field of optical modulation technology.
The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in ...Ophthalmology. A training dataset consisted of 1,364 color fundus photographs with glaucomatous indications and 1,768 color fundus photographs without glaucomatous features. A testing dataset consisted of 60 eyes of 60 glaucoma patients and 50 eyes of 50 normal subjects. Using the training dataset, a deep learning algorithm known as Deep Residual Learning for Image Recognition (ResNet) was developed to discriminate glaucoma, and its diagnostic accuracy was validated in the testing dataset, using the area under the receiver operating characteristic curve (AROC). The Deep Residual Learning for Image Recognition was constructed using the training dataset and validated using the testing dataset. The presence of glaucoma in the testing dataset was also confirmed by three Residents in Ophthalmology. The deep learning algorithm achieved significantly higher diagnostic performance compared to Residents in Ophthalmology; with ResNet, the AROC from all testing data was 96.5 (95% confidence interval CI: 93.5 to 99.6)% while the AROCs obtained by the three Residents were between 72.6% and 91.2%.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
To differentiate the visual fields (VFs) of preperimetric open-angle glaucoma (OAG) patients from the VFs of healthy eyes using a deep learning (DL) method.
Cohort study.
One hundred seventy-one ...preperimetric glaucoma VFs (PPGVFs) from 53 eyes in 51 OAG patients and 108 healthy eyes of 87 healthy participants.
Preperimetric glaucoma VFs were defined as all VFs before a first diagnosis of manifest glaucoma (Anderson-Patella's criteria). In total, 171 PPGVFs from 53 eyes in 51 OAG patients and 108 VFs from 108 healthy eyes in 87 healthy participants were analyzed (all VFs were tested using the Humphrey Field Analyzer 30-2 program; Carl Zeiss Meditec, Dublin, CA). The 52 total deviation, mean deviation, and pattern standard deviation values were used as predictors in the DL classifier: a deep feed-forward neural network (FNN), along with other machine learning (ML) methods, including random forests (RF), gradient boosting, support vector machine, and neural network (NN). The area under the receiver operating characteristic curve (AUC) was used to evaluate the accuracy of discrimination for each method.
The AUCs obtained with each classifier method.
A significantly larger AUC of 92.6% (95% confidence interval CI, 89.8%-95.4%) was obtained using the deep FNN classifier compared with all other ML methods: 79.0% (95% CI, 73.5%-84.5%) with RF, 77.6% (95% CI, 71.7%-83.5%) with gradient boosting, 71.2% (95% CI, 65.0%-77.5%), and 66.7% (95% CI, 60.1%-73.3%) with NN.
Preperimetric glaucoma VFs can be distinguished from healthy VFs with very high accuracy using a deep FNN classifier.
We sought to construct and evaluate a deep learning (DL) model to diagnose early glaucoma from spectral-domain optical coherence tomography (OCT) images.
Artificial intelligence diagnostic tool ...development, evaluation, and comparison.
This multi-institution study included pretraining data of 4316 OCT images (RS3000) from 1371 eyes with open angle glaucoma (OAG) regardless of the stage of glaucoma and 193 normal eyes. Training data included OCT-1000/2000 images from 94 eyes of 94 patients with early OAG (mean deviation > −5.0 dB) and 84 eyes of 84 normal subjects. Testing data included OCT-1000/2000 from 114 eyes of 114 patients with early OAG (mean deviation > −5.0 dB) and 82 eyes of 82 normal subjects. A DL (convolutional neural network) classifier was trained using a pretraining dataset, followed by a second round of training using an independent training dataset. The DL model input features were the 8 × 8 grid macular retinal nerve fiber layer thickness and ganglion cell complex layer thickness from spectral-domain OCT. Diagnostic accuracy was investigated in the testing dataset. For comparison, diagnostic accuracy was also evaluated using the random forests and support vector machine models. The primary outcome measure was the area under the receiver operating characteristic curve (AROC).
The AROC with the DL model was 93.7%. The AROC significantly decreased to between 76.6% and 78.8% without the pretraining process. Significantly smaller AROCs were obtained with random forests and support vector machine models (82.0% and 67.4%, respectively).
A DL model for glaucoma using spectral-domain OCT offers a substantive increase in diagnostic performance.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
We evaluated effectiveness of tea tree oil (TO) and lemongrass oil (LO) for removal of Candida biofilm from denture base resin and their influence on that surface. Biofilm of C. albicans was formed ...on resins, and immersed in various concentrations of each oil and distilled water (DW). The biofilm removal effect was determined by incubating specimens in RPMI medium containing Alamar blue (AB) and measuring absorbance. Wear test was also conducted, and surface condition of resins was determined using laser scanning microscope and digital microscope. Specimens immersed in the TO and LO solutions tended to have a lower AB value at higher concentrations and longer soaking times. Use of these agents resulted in less surface roughness as compared to DW. Our results suggest that TO and LO were valid to remove biofilm attached to resin with lower levels of abrasion, and these are effective for use in denture cleaner.
Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study ...introduces a machine learning–based index for glaucoma progression detection that outperforms global, region-wise, and point-wise indices.
Development and comparison of a prognostic index.
Visual fields from 2085 eyes of 1214 subjects were used to identify glaucoma progression patterns using machine learning. Visual fields from 133 eyes of 71 glaucoma patients were collected 10 times over 10 weeks to provide a no-change, test-retest dataset. The parameters of all methods were identified using visual field sequences in the test-retest dataset to meet fixed 95% specificity. An independent dataset of 270 eyes of 136 glaucoma patients and survival analysis were used to compare methods.
The time to detect progression in 25% of the eyes in the longitudinal dataset using global mean deviation (MD) was 5.2 (95% confidence interval, 4.1–6.5) years; 4.5 (4.0–5.5) years using region-wise, 3.9 (3.5–4.6) years using point-wise, and 3.5 (3.1–4.0) years using machine learning analysis. The time until 25% of eyes showed subsequently confirmed progression after 2 additional visits were included were 6.6 (5.6–7.4) years, 5.7 (4.8–6.7) years, 5.6 (4.7–6.5) years, and 5.1 (4.5–6.0) years for global, region-wise, point-wise, and machine learning analyses, respectively.
Machine learning analysis detects progressing eyes earlier than other methods consistently, with or without confirmation visits. In particular, machine learning detects more slowly progressing eyes than other methods.
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We evaluated the usefulness of various regression models, including least absolute shrinkage and selection operator (Lasso) regression, to predict future visual field (VF) progression in glaucoma ...patients.
Series of 10 VFs (Humphrey Field Analyzer 24-2 SITA-standard) from each of 513 eyes in 324 open-angle glaucoma patients, obtained in 4.9 ± 1.3 years (mean ± SD), were investigated. For each patient, the mean of all total deviation values (mTD) in the 10th VF was predicted using varying numbers of prior VFs (ranging from the first three VFs to all previous VFs) by applying ordinary least squares linear regression (OLSLR), M-estimator robust regression (M-robust), MM-estimator robust regression (MM-robust), skipped regression (Skipped), deepest regression (Deepest), and Lasso regression. Absolute prediction errors then were compared.
With OLSLR, prediction error varied between 5.7 ± 6.1 (using the first three VFs) and 1.2 ± 1.1 dB (using all nine previous VFs). Prediction accuracy was not significantly improved with M-robust, MM-robust, Skipped, or Deepest regression in almost all VF series; however, a significantly smaller prediction error was obtained with Lasso regression even with a small number of VFs (using first 3 VFs, 2.0 ± 2.2; using all nine previous VFs, 1.2 ± 1.1 dB).
Prediction errors using OLSLR are large when only a small number of VFs are included in the regression. Lasso regression offers much more accurate predictions, especially in short VF series.
The purpose of the current study was to evaluate the test-retest reproducibility and structure-function relationship of the MP-3 microperimeter, compared against the Humphrey Field Analyzer (HFA).
...Design: Reliability and validity study. Setting: Institutional, or clinical practice. Study Population: Thirty eyes of 30 primary open-angle glaucoma patients were enrolled. Observation Procedures: Visual fields (VF) were measured twice with the MP-3 and HFA instruments, using the 10-2 test grid pattern in both perimeters. Ganglion cell complex (GCC) thickness was measured using optical coherence tomography (OCT). Test-retest reproducibility was assessed using the mean absolute deviation (MAD) measure at all 68 VF test points, and also the intraclass correlation coefficient (ICC) of the repeated VF sensitivities. The structure-function relationship between VF sensitivities (measured with MP-3 or HFA) and GCC thickness (adjusted for the retinal ganglion cell displacement) was analyzed using linear mixed modeling. Main Outcome Measure: Reproducibility and structure-function relationship.
The average measurement duration with the HFA 10-2 was 7 minutes and 6 seconds (7m06s) ± 0m49s (mean ± standard deviation). A significantly (P < .001, paired Wilcoxon test) longer measurement duration was observed for the MP-3 test: 10m29s ± 2m55s. There were no significant differences in MAD and ICC values between HFA (MAD; 0.83 ± 0.69 dB and ICC: 0.89 ± 0.69, mean ± standard deviation) and MP-3 (MAD: 0.65 ± 0.67 dB and ICC: 0.89 ± 0.69). MP-3 VF sensitivities had a stronger structure-function relationship with GCC thickness compared to HFA.
The MP-3 microperimeter has a similar test-retest reproducibility to the HFA but a better structure-function relationship.
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We evaluated the usefulness of gaze tracking (GT) results as an index of visual field reliability in glaucoma.
The study population consisted of 631 eyes of 400 patients with open angle glaucoma in ...an institutional practice, with 10 visual fields (VFs). For the observational procedure, visual fixation was assessed using the gaze fixation chart at the bottom of the VF (Humphrey Field Analyzer, 30-2 SITA standard) printout. Average frequency of eye movement between 1° and 2° (move(1-2)), 3° and 5° (move(3-5)), and greater than or equal to 6° (move(≥6)) were calculated. In addition, average tracking failure frequency (TFF) and average blinking frequency (BF) were calculated. The relationship between mean deviation (MD), fixation losses (FLs), false-positives (FPs), false-negatives (FNs), move(1-2), move(3-5), move(≥6), TFF, BF, and pattern standard deviation (PSD) were evaluated using linear modeling. Main outcome measures included parameters related to over- or underestimation of MD values.
Patients' mean MD progression rate was -0.23 dB/y. The best model to predict MD values included FL rate, FP rate, move(3-5), move(≥6), TFF, BF, and PSD as dependent variables with coefficients of 0.90, 9.2, -0.57, -0.52, -2.2, -1.1, and -0.56, respectively (P < 0.001).
High FL and FP rates tend to raise MD values. By contrast, high values of move(3-5), move(≥6), TFF, BF, and PSD tend to lower MD values. Thus, GT parameters can be used as new indices of VF reliability through the prediction of over- or underestimation of VF results.
The purpose of this study was to investigate how mild-to-moderate myopia and aging affect visual field sensitivity (VF-S) in normal eyes, correcting for effects of each.
Combined cross-sectional and ...cohort study.
Two normal groups, a cross-sectional group (n = 703; 1,051 eyes; mean age, 52.6 years) and a longitudinal group (n = 44; 83 eyes; mean age, 52.3 years; follow-up, 4.2 years; VF tests, 12) were included. In the cross-sectional group, the mean VF-S of the entire field and 3 disc portion-oriented subfields of the Humphrey Field Analyzer 24-2 program were correlated with subjects’ age, axial length (AL), disc, rim and β-peripapillary area, and disc ovality and torsion, using linear mixed-regression models. Their time changes in the longitudinal group were correlated with time, subjects’ ages, and AL using linear mixed-regression models.
In the cross-sectional group, the VF-S correlated negatively with age (−0.081 decibel dB/year; P < .001), which was more negative (P = .020) in the midperipheral than the central subfield, and with AL (P = .049) without intersubfield differences. In the longitudinal group, no changes in the ocular media were significant, and the VF-S declined by 0.074 dB/year (P = .007), which accelerated with higher age (P < .002) and baseline VF-S (P < .001) without intersubfield differences. The AL showed little effects on the VF-S longitudinal changes.
In normal eyes with mild-to-moderate myopia, the VF-S was lower subfield-independently with longer AL, whereas the AL had little effect on the aging-associated VF-S reduction. The VF-S decreased with aging with intersubfield differences. The aging-associated VF-S reduction accelerated with higher age, to which the ocular media changes were unrelated.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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