1. Articles from Ryo Asaoka

    1-16 of 16
    1. Predicting retinal sensitivity using optical coherence tomography parameters in central serous chorioretinopathy

      Predicting retinal sensitivity using optical coherence tomography parameters in central serous chorioretinopathy

      Purpose To predict changes in retinal sensitivity using optical coherence tomography (OCT) in eyes with central serous chorioretinopathy (CSC). Methods Twenty-three eyes in 23 patients with CSC were enrolled. Retinal sensitivity was measured twice using microperimetry in all the examined eyes. Spectral domain OCT measurements were simultaneously conducted. The relationship between retinal sensitivity and the thicknesses of (i) the retinal nerve fiber layer plus the ganglion cell layer (RNFL + GCL), (ii) the inner nuclear layer (INL), (iii) the outer nuclear layer (ONL), and (iv) the serous retinal detachment height (SRDH) were investigated in a point-wise manner. The associations between the ...

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    2. Corneal Irregular Astigmatism And Visual Function On Anterior Segment Optical Coherence Tomography In TGFBI Corneal Dystrophy

      Corneal Irregular Astigmatism And Visual Function On Anterior Segment Optical Coherence Tomography In TGFBI Corneal Dystrophy

      The purpose of this study was to evaluate corneal irregular astigmatism of patients with granular and lattice corneal dystrophy (GCD and LCD). 70 GCD, 35 LCD, and 23 control eyes were included. Anterior and posterior corneal topographic data obtained from anterior segment optical coherence tomography were expanded into four components via Fourier harmonic analysis. These components were compared with healthy eyes and the association between each component and best-corrected visual acuity (BCVA) was investigated. Anterior and posterior components increased in LCD eyes, but none increased in GCD. Posterior components of GCD type 2 (GCD2), anterior and posterior of LCD type ...

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    3. Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field

      Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field

      Purpose: To investigate whether a correction based on a Humphrey field analyzer (HFA) 24-2/30-2 visual field (VF) can improve the prediction performance of a deep learning model to predict the HFA 10-2 VF test from macular optical coherence tomography (OCT) measurements. Methods: This is a multicenter, cross-sectional study. The training dataset comprised 493 eyes of 285 subjects (407, open-angle glaucoma [OAG]; 86, normative) who underwent HFA 10-2 testing and macular OCT. The independent testing dataset comprised 104 OAG eyes of 82 subjects who had undergone HFA 10-2 test, HFA 24-2/30-2 test, and macular OCT. A convolutional neural network ...

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    4. Relationship Between Optical Coherence Tomography Parameter and Visual Function in Eyes With Epiretinal Membrane

      Relationship Between Optical Coherence Tomography Parameter and Visual Function in Eyes With Epiretinal Membrane

      Purpose: To investigate the associations between visual function and the optical coherence tomography (OCT) parameters in eyes with idiopathic epiretinal membrane (ERM). Methods: Thirty-nine consecutive eyes with ERM were enrolled. In addition to OCT parameters, such as central retinal thickness (CRT), the area of gap between the ERM and the retinal surface (SUKIMA) was newly defined and calculated from the vertical and horizontal OCT images (SUKIMAv and SUKIMAh). The average of SUKIMAv and SUKIMAh (SUKIMAave) was used for the statistical analysis. The vertical and horizontal metamorphopsia scores (MV, MH) and the average of MV and MH (Mave) were also used ...

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    5. Macular irregularities of optical coherence tomographic vertical cross sectional images in school age children

      Macular irregularities of optical coherence tomographic vertical cross sectional images in school age children

      The purpose of this study was to compare the incidences of macular irregularities of elementary school (ES) and junior high school (JHS) students. This was a prospective cross-sectional observational study of 122 right eyes of 122 ES students (8-9 years) and 173 right eyes of 173 JHS students (12-13 years). Vertical cross-sectional images of the macula were obtained by optical coherence tomography. The eyes were classified based on the vertical symmetry of the posterior pole, and then sub-classified as convex-, flat-, concave-, or dome-shaped based on the direction of the curvature of the retinal pigment epithelium. One hundred and two ...

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    6. Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images

      Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images

      We aimed to develop a model to predict visual field (VF) in the central 10 degrees in patients with glaucoma, by training a convolutional neural network (CNN) with optical coherence tomography (OCT) images and adjusting the values with Humphrey Field Analyzer (HFA) 24-2 test. The training dataset included 558 eyes from 312 glaucoma patients and 90 eyes from 46 normal subjects. The testing dataset included 105 eyes from 72 glaucoma patients. All eyes were analyzed by the HFA 10-2 test and OCT; eyes in the testing dataset were additionally analyzed by the HFA 24-2 test. During CNN model training, the ...

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    7. Improving visual field trend analysis with optical coherence tomography and deeply-regularized latent-space linear regression

      Improving visual field trend analysis with optical coherence tomography and deeply-regularized latent-space linear regression

      Purpose To investigate whether optical coherence tomography (OCT) measurements can improve visual field (VF) trend analyses in glaucoma patients, using the ‘deeply-regularized latent-space linear regression’ (DLLR) model. Design Retrospective cohort study Subjects Training and testing datasets included 7,984 VFs from 998 eyes of 592 patients and 1,184 VFs from 148 eyes of 84 patients with open angle glaucoma, respectively. Each eye had a series of eight VFs with the Humphrey Field Analyzer. OCT series were obtained within the same observation period. Methods Using pointwise linear regression (PLR), the threshold values of a patient’s eighth VF were predicted ...

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    8. Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma

      Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma

      Background/Aim To train and validate the prediction performance of the deep learning (DL) model to predict visual field (VF) in central 10° from spectral domain optical coherence tomography (SD-OCT). Methods This multicentre, cross-sectional study included paired Humphrey field analyser (HFA) 10-2 VF and SD-OCT measurements from 591 eyes of 347 patients with open-angle glaucoma (OAG) or normal subjects for the training data set. We trained a convolutional neural network (CNN) for predicting VF threshold (TH) sensitivity values from the thickness of the three macular layers: retinal nerve fibre layer, ganglion cell layer+inner plexiform layer and outer segment+retinal ...

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    9. Predicting the glaucomatous central 10 degrees visual field from optical coherence tomography using deep learning and tensor regression

      Predicting the glaucomatous central 10 degrees visual field from optical coherence tomography using deep learning and tensor regression

      Purpose To predict the visual field (VF) of glaucoma patients within the central 10 degrees from optical coherence tomography (OCT) measurements using deep learning and tensor regression. Design cross-sectional study Method Humphrey 10-2 VFs and OCT measurements were carried out in 505 eyes of 304 glaucoma patients and 86 eyes of 43 normal subjects. VF sensitivity at each test point was predicted from OCT-measured thicknesses of macular ganglion cell layer + inner plexiform layer, retinal nerve fiber layer, and outer segment + retinal pigment epithelium. Two convolutional neural network (CNN) models were generated: (1) ‘CNN-PR’ which simply connects the output of the ...

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    10. Using Deep Learning and transform learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images

      Using Deep Learning and transform learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images

      Purpose To construct and evaluate a Deep Learning (DL) model to diagnose early glaucoma from spectral domain optical coherence tomography (SD-OCT) images. Design AI diagnostic tool development, evaluation, and comparison Methods Setting: multiple institutional practices. Study population Pre-training data consisted of 4316 OCT images (RS3000, Nidek) from 1565 eyes with open angle glaucoma (OAG) irrespective of the stage of glaucoma and 193 normal eyes. Training data included OCT-1000/2000 (Topcon) from 94 eyes of 94 early OAG patients (mean deviation: MD >-5.0 dB) and 84 eyes of 84 normal subjects. Testing data included OCT-1000/2000 from 114 eyes of ...

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    11. The structure-function relationship measured with optical coherence tomography and a microperimeter with auto-tracking: the MP-3, in patients with retinitis pigmentosa

      The structure-function relationship measured with optical coherence tomography and a microperimeter with auto-tracking: the MP-3, in patients with retinitis pigmentosa

      The purpose of the current study was to investigate the structure-function relationship in patients with retinitis pigmentosa (RP) using optical coherence tomography and the MP-3 microperimeter. Visual field (VF) measurements were carried out using MP-3 microperimetry and the Humphrey Field Analyzer (HFA, Carl-Zeiss, CA), 22 eyes of 11 patients with a clinical diagnosis of RP, both with the 10-2 test grid pattern. Optical coherence tomography (OCT, Spectralis, Heidelberg, Germany) was also performed and the ellipsoid zone (EZ) was identified in the OCT image. The mean (±SD) number of test points located within the EZ edge was 11.6 (±5.9 ...

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    12. The association between photoreceptor layer thickness measured by optical coherence tomography and visual sensitivity in glaucomatous eyes

      The association between photoreceptor layer thickness measured by optical coherence tomography and visual sensitivity in glaucomatous eyes

      Purpose To assess the thickness of the photoreceptor layer in the macular region in glaucomatous eyes. Method 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 ...

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    13. Validating the usefulness of the 'Random Forests’ classifier to diagnose early glaucoma with optical coherence tomography

      Validating the usefulness of the 'Random Forests’ classifier to diagnose early glaucoma with optical coherence tomography

      Purpose To validate the usefulness of the 'Random Forests’ classifier to diagnose early glaucoma with spectral domain optical coherence tomography (SD-OCT). Method Design: Comparison of diagnostic algorithms Setting: multiple institutional practice Study participants Training dataset included 94 eyes of 94 open angle glaucoma (OAG) patients and 84 eyes of 84 normal subjects and testing dataset included 114 eyes of 114 OAG patients and 82 eyes of 82 normal subjects. In both groups, OAG eyes with mean deviation (MD) values better than -5.0 dB were included. Observation Procedure Using the training dataset, classifiers were built to discriminate between glaucoma and ...

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    14. Discriminating between Glaucoma and Normal Eyes Using Optical Coherence Tomography and the ‘Random Forests’ Classifier

      Discriminating between Glaucoma and Normal Eyes Using Optical Coherence Tomography and the ‘Random Forests’ Classifier

      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 ...

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    15. 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

      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

      Objectives To develop a classifier to predict the presence of visual field (VF) deterioration in glaucoma suspects based on optical coherence tomography (OCT) measurements using the machine learning method known as the ‘Random Forest’ algorithm. Design Case–control study. Participants 293 eyes of 179 participants with open angle glaucoma (OAG) or suspected OAG. Interventions Spectral domain OCT (Topcon 3D OCT-2000) and perimetry (Humphrey Field Analyser, 24-2 or 30-2 SITA standard) measurements were conducted in all of the participants. VF damage (Ocular Hypertension Treatment Study criteria (2002)) was used as a ‘gold-standard’ to classify glaucomatous eyes. The ‘Random Forest’ method was ...

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    16. Relationship between position of peak retinal nerve fiber layer thickness and retinal arteries on sectoral retinal nerve fiber layer thickness

      Relationship between position of peak retinal nerve fiber layer thickness and retinal arteries on sectoral retinal nerve fiber layer thickness

      PURPOSE: To determine the relationship between the position of the peak of the retinal nerve fiber layer (RNFL) thickness and the retinal arteries, axial length (AL), and sectoral RNFL thickness in healthy eyes. METHODS: A prospective, observational cross-sectional study (registration number, UMIN000006040) of 50 healthy right eyes (mean age 25.8 ± 3.7 years) was performed. The RNFL thickness was measured by optical coherence tomography in twelve 30-degrees sectors (clock hours) around the optic disc. The RNFL nasal-superior-temporal-inferior-nasal curves and fundus photographs were used to measure the angles between the supra-temporal and the infra-temporal peak RNFL positions (peak angle) and ...

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    1-16 of 16
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    1. (8 articles) University of Tokyo
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    Relationship between position of peak retinal nerve fiber layer thickness and retinal arteries on sectoral retinal nerve fiber layer thickness 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 Discriminating between Glaucoma and Normal Eyes Using Optical Coherence Tomography and the ‘Random Forests’ Classifier Validating the usefulness of the 'Random Forests’ classifier to diagnose early glaucoma with optical coherence tomography The association between photoreceptor layer thickness measured by optical coherence tomography and visual sensitivity in glaucomatous eyes The structure-function relationship measured with optical coherence tomography and a microperimeter with auto-tracking: the MP-3, in patients with retinitis pigmentosa Using Deep Learning and transform learning to accurately diagnose early-onset glaucoma from macular optical coherence tomography images Predicting the glaucomatous central 10 degrees visual field from optical coherence tomography using deep learning and tensor regression Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma Improving visual field trend analysis with optical coherence tomography and deeply-regularized latent-space linear regression Optical coherence tomography findings in patients with transfusion-dependent β-thalassemia Higher-order regression three-dimensional motion-compensation method for real-time optical coherence tomography volumetric imaging of the cornea