Glaucoma, Open-Angle

Improving the Structure-Function Relationship in Glaucomatous Visual Fields by Using a Deep Learning-Based Noise Reduction Approach.

Ophthalmology. Glaucoma 32672618

PURPOSE: To investigate whether processing visual field (VF) measurements using a variational autoencoder (VAE) improves the structure-function relationship in glaucoma.

Cross-Sectional Studies Deep Learning Female Glaucoma, Open-Angle Humans Intraocular Pressure Male Middle Aged Nerve Fibers Optic Disk Retinal Ganglion Cells Structure-Activity Relationship Tomography, Optical Coherence Visual Fields

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Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans.

JAMA ophthalmology 32053142

IMPORTANCE: Conventional segmentation of the retinal nerve fiber layer (RNFL) is prone to errors that may affect the accuracy of spectral-domain optical coherence tomography (SD-OCT) scans in detecting glaucomatous damage.

Adult Aged Aged, 80 and over Algorithms Cross-Sectional Studies Deep Learning Female Glaucoma, Open-Angle Gonioscopy Humans Intraocular Pressure Male Middle Aged Nerve Fibers Retinal Ganglion Cells ROC Curve Slit Lamp Microscopy Tomography, Optical Coherence Visual Field Tests Visual Fields

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The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields.

Scientific reports 32398783

The aim of the study was to investigate the usefulness of processing visual field (VF) using a variational autoencoder (VAE). The training data consisted of 82,433 VFs from 16,836 eyes. Testing dataset 1 consisted of test-retest VFs from 104 eyes wit...

Adult Deep Learning Disease Progression Eye Female Glaucoma, Open-Angle Humans Intraocular Pressure Linear Models Male Middle Aged Reproducibility of Results Sensitivity and Specificity Visual Field Tests Visual Fields

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Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression.

American journal of ophthalmology 32387432

PURPOSE: To predict the visual field (VF) of glaucoma patients within the central 10° from optical coherence tomography (OCT) measurements using deep learning and tensor regression.

Adult Aged Axial Length, Eye Cross-Sectional Studies Deep Learning Female Glaucoma, Open-Angle Gonioscopy Humans Intraocular Pressure Male Middle Aged Optic Nerve Diseases Retrospective Studies Sensitivity and Specificity Slit Lamp Microscopy Tomography, Optical Coherence Vision Disorders Visual Acuity Visual Field Tests Visual Fields

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OCT Signal Enhancement with Deep Learning.

Ophthalmology. Glaucoma 33069883

PURPOSE: To establish whether deep learning methods are able to improve the signal-to-noise ratio of time-domain (TD) OCT images to approach that of spectral-domain (SD) OCT images.

Bayes Theorem Deep Learning Double-Blind Method Glaucoma, Open-Angle Humans Tomography, Optical Coherence

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Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL.

Scientific reports 33149191

We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch's membrane opening-minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer (RNFL), and the color classification of RNFL based on a deep-learni...

Adult Area Under Curve Bruch Membrane Deep Learning Diagnostic Techniques, Ophthalmological Female Glaucoma, Open-Angle Humans Image Processing, Computer-Assisted Intraocular Pressure Low Tension Glaucoma Male Middle Aged Retina ROC Curve Tomography, Optical Coherence

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Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.

Ophthalmology 33359887

PURPOSE: Rule-based approaches to determining glaucoma progression from visual fields (VFs) alone are discordant and have tradeoffs. To detect better when glaucoma progression is occurring, we used a longitudinal data set of merged VF and clinical da...

Aged Algorithms Clinical Competence Disease Progression Female Follow-Up Studies Glaucoma Glaucoma, Open-Angle Humans Intraocular Pressure Machine Learning Male Ophthalmologists Retrospective Studies ROC Curve Visual Fields

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Machine Learning Approach for Intraocular Disease Prediction Based on Aqueous Humor Immune Mediator Profiles.

Ophthalmology 33484732

PURPOSE: Various immune mediators have crucial roles in the pathogenesis of intraocular diseases. Machine learning can be used to automatically select and weigh various predictors to develop models maximizing predictive power. However, these techniqu...

Adult Aged Aged, 80 and over Aqueous Humor Area Under Curve Cross-Sectional Studies Diagnosis, Computer-Assisted Endophthalmitis Eye Diseases Female Flow Cytometry Glaucoma, Open-Angle Humans Immunoassay Inflammation Mediators Interleukins Intraocular Lymphoma Machine Learning Male Middle Aged Reproducibility of Results Retinal Detachment Retinal Necrosis Syndrome, Acute ROC Curve

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Aqueous autotaxin and TGF-βs are promising diagnostic biomarkers for distinguishing open-angle glaucoma subtypes.

Scientific reports 33446826

The purpose of this study is to examine if aqueous autotaxin (ATX) and TGF-β levels could be used for differentiating glaucoma subtypes. This prospective observational study was performed using aqueous humor samples obtained from 281 consecutive pati...

Adult Aged Aged, 80 and over Aqueous Humor Biomarkers Female Glaucoma, Open-Angle Humans Machine Learning Male Middle Aged Phosphoric Diester Hydrolases Prospective Studies Transforming Growth Factor beta

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Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology.

American journal of human genetics 34077760

Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color ...

Datasets as Topic Fluorescein Angiography Genome-Wide Association Study Glaucoma, Open-Angle Humans Machine Learning Models, Anatomic Optic Disk Phenotype Risk Assessment

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Explore the latest research on artificial intelligence and machine learning in medicine.

Improving the Structure-Function Relationship in Glaucomatous Visual Fields by Using a Deep Learning-Based Noise Reduction Approach.

Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans.

The usefulness of the Deep Learning method of variational autoencoder to reduce measurement noise in glaucomatous visual fields.

Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression.

OCT Signal Enhancement with Deep Learning.

Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL.

Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.

Machine Learning Approach for Intraocular Disease Prediction Based on Aqueous Humor Immune Mediator Profiles.

Aqueous autotaxin and TGF-βs are promising diagnostic biomarkers for distinguishing open-angle glaucoma subtypes.

Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology.

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