Cargando…

Comparing the usefulness of a new algorithm to measure visual field using the variational Bayes linear regression in glaucoma patients, in comparison to the Swedish interactive thresholding algorithm

BACKGROUND/AIMS: We previously reported that the visual field (VF) prediction model using the variational Bayes linear regression (VBLR) is useful for accurately predicting VF progression in glaucoma (Invest Ophthalmol Vis Sci. 2014, 2018). We constructed a VF measurement algorithm using VBLR, and t...

Descripción completa

Detalles Bibliográficos
Autores principales:	Murata, Hiroshi, Asaoka, Ryo, Fujino, Yuri, Matsuura, Masato, Hirasawa, Kazunori, Shimada, Satoshi, Shoji, Nobuyuki
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BMJ Publishing Group 2022
Materias:	Clinical Science
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046736/ https://www.ncbi.nlm.nih.gov/pubmed/33441321 http://dx.doi.org/10.1136/bjophthalmol-2020-318304

Ejemplares similares

Comparing the structure-function relationship between the visual fields measured with variational Bayes linear regression and SITA standard
por: Asaoka, Ryo, et al.
Publicado: (2023)

Validating Variational Bayes Linear Regression Method With Multi-Central Datasets
por: Murata, Hiroshi, et al.
Publicado: (2018)

Using CorvisST tonometry to assess glaucoma progression
por: Matsuura, Masato, et al.
Publicado: (2017)

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography
por: Shibata, Naoto, et al.
Publicado: (2018)

The usefulness of CorvisST Tonometry and the Ocular Response Analyzer to assess the progression of glaucoma
por: Matsuura, Masato, et al.
Publicado: (2017)

Goldmann V Standard Automated Perimetry Underestimates Central Visual Sensitivity in Glaucomatous Eyes with Increased Axial Length
por: Yanagisawa, Mieko, et al.
Publicado: (2017)

A Comparative Study between Swedish Interactive Thresholding Algorithm Faster and Swedish Interactive Thresholding Algorithm Standard in Glaucoma Patients
por: Mendieta, Núria, et al.
Publicado: (2021)

The Relationship between Corvis ST Tonometry and Ocular Response Analyzer Measurements in Eyes with Glaucoma
por: Matsuura, Masato, et al.
Publicado: (2016)

The number of examinations required for the accurate prediction of the progression of the central 10-degree visual field test in glaucoma
por: Omoto, Takashi, et al.
Publicado: (2022)

Improving the structure-function relationship in glaucomatous and normative eyes by incorporating photoreceptor layer thickness
por: Matsuura, Masato, et al.
Publicado: (2018)

Biomechanical Glaucoma Factor and Corneal Hysteresis in Treated Primary Open-Angle Glaucoma and Their Associations With Visual Field Progression
por: Aoki, Shuichiro, et al.
Publicado: (2021)

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms
por: Christopher, Mark, et al.
Publicado: (2020)

The relationship between retinal nerve fibre layer thickness profiles and CorvisST tonometry measured biomechanical properties in young healthy subjects
por: Matsuura, Masato, et al.
Publicado: (2017)

Sensitivity and Specificity of Swedish Interactive Threshold Algorithm and Standard Full Threshold Perimetry in Primary Open-angle Glaucoma
por: BAMDAD, Shahram, et al.
Publicado: (2017)

Assessing Visual Fields in Patients with Retinitis Pigmentosa Using a Novel Microperimeter with Eye Tracking: The MP-3
por: Igarashi, Nozomi, et al.
Publicado: (2016)

Diagnostic Ability and Repeatability of a New Supra-Threshold Glaucoma Screening Program in Standard Automated Perimetry
por: Takahashi, Natsumi, et al.
Publicado: (2017)

Association between Corneal Biomechanical Properties with Ocular Response Analyzer and Also CorvisST Tonometry, and Glaucomatous Visual Field Severity
por: Hirasawa, Kazunori, et al.
Publicado: (2017)

Comparison of Different Analytic Algorithms for Interpretation of the Swedish Interactive Threshold Algorithm Strategy
por: Takahashi, Gustavo S., et al.
Publicado: (2008)

Comparing Structure-Function Relationships Based on Drasdo's and Sjöstrand's Retinal Ganglion Cell Displacement Models
por: Hirasawa, Kazunori, et al.
Publicado: (2020)

Discriminating between Glaucoma and Normal Eyes Using Optical Coherence Tomography and the ‘Random Forests’ Classifier
por: Yoshida, Tatsuya, et al.
Publicado: (2014)

The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields
por: Asaoka, Ryo, et al.
Publicado: (2020)

Correlation between elastic energy stored in an eye and visual field progression in glaucoma
por: Aoki, Shuichiro, et al.
Publicado: (2018)

Detecting Progression of Retinitis Pigmentosa Using the Binomial Pointwise Linear Regression Method
por: Asano, Shotaro, et al.
Publicado: (2021)

Impact of better and worse eye damage on quality of life in advanced glaucoma
por: Okamoto, Mizu, et al.
Publicado: (2014)

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?
por: Sugimoto, Koichiro, et al.
Publicado: (2013)

Diagnostic capability of Pulsar perimetry in pre-perimetric and early glaucoma
por: Hirasawa, Kazunori, et al.
Publicado: (2017)

Identifying Areas of the Visual Field Important for Quality of Life in Patients with Glaucoma
por: Murata, Hiroshi, et al.
Publicado: (2013)

Predicting the central 10 degrees visual field in glaucoma by applying a deep learning algorithm to optical coherence tomography images
por: Asano, Shotaro, et al.
Publicado: (2021)

Comparison of Matrix Frequency-Doubling Technology (FDT) Perimetry with the SWEDISH Interactive Thresholding Algorithm (SITA) Standard Automated Perimetry (SAP) in Mild Glaucoma
por: DOOZANDEH, Azadeh, et al.
Publicado: (2017)

Smaller Fixation Target Size Is Associated with More Stable Fixation and Less Variance in Threshold Sensitivity
por: Hirasawa, Kazunori, et al.
Publicado: (2016)

Comparison of two analyzer measurements focusing on material stiffness among normal, treatment-naïve, and treated glaucoma eyes
por: Aoki, Shuichiro, et al.
Publicado: (2023)

Correction: Identifying Areas of the Visual Field Important for Quality of Life in Patients with Glaucoma
por: Murata, Hiroshi, et al.
Publicado: (2015)

Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field
por: Hashimoto, Yohei, et al.
Publicado: (2021)

Sampling algorithm for estimating Bayes factor
por: Borgos, H G, et al.
Publicado: (2000)

Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle
por: Asano, Shotaro, et al.
Publicado: (2020)

Relationship Between Macular Ganglion Cell Thickness and Ocular Elongation as Measured by Axial Length and Retinal Artery Position
por: Omoto, Takashi, et al.
Publicado: (2020)

Structure–Function Relationships and Glaucoma Detection with Magnification Correction of OCT Angiography
por: Hirasawa, Kazunori, et al.
Publicado: (2022)

Comparison of size modulation and conventional standard automated perimetry with the 24-2 test protocol in glaucoma patients
por: Hirasawa, Kazunori, et al.
Publicado: (2016)

Comparison of Humphrey Field Analyzer and imo visual field test results in patients with glaucoma and pseudo-fixation loss
por: Goukon, Hiroyasu, et al.
Publicado: (2019)

The association between photoreceptor layer thickness measured by optical coherence tomography and visual sensitivity in glaucomatous eyes
por: Asaoka, Ryo, et al.
Publicado: (2017)

Cannot write session to /tmp/vufind_sessions/sess_9pn3noe31rp5i6ppa46g4k9s56