Yousefi Siamak

Relationship between Optical Coherence Tomography Angiography Vessel Density and Severity of Visual Field Loss in Glaucoma.

Decreased vessel density was significantly associated with the severity of visual field damage independent of the structural loss.

Optical Coherence Tomography Angiography Vessel Density in Glaucomatous Eyes with Focal Lamina Cribrosa Defects.

In eyes with similar severity of glaucoma, OCT-A-measured vessel density was significantly lower in POAG eyes with focal LC defects than in eyes without an LC defect.

Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.

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.

Asymmetric Patterns of Visual Field Defect in Primary Open-Angle and Primary Angle-Closure Glaucoma.

In both POAG and PACG eyes, VF damage was more pronounced in superior hemifield than inferior hemifield; however, this tendency was more obvious in POAG eyes than in PACG eyes.

Prediction of Visual Field Progression from OCT Structural Measures in Moderate to Advanced Glaucoma.

VF progression can be predicted with clinically relevant accuracy from baseline and longitudinal structural data. Further refinement of proposed models would assist clinicians with timely prediction of functional glaucoma progression and clinical decision making.

Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.

The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous…

Machine-Identified Patterns of Visual Field Loss and an Association with Rapid Progression in the Ocular Hypertension Treatment Study.

An automated machine learning system can identify patterns of VF loss and could provide objective and reproducible nomenclature for characterizing early signs of visual defects and rapid progression in patients with glaucoma.

Predicting Glaucoma Before Onset Using a Large Language Model Chatbot.

The performance of ChatGPT4.0 in forecasting development of glaucoma 1 year before onset was reasonable.

An Artificial Intelligence Approach to Assess Spatial Patterns of Retinal Nerve Fiber Layer Thickness Maps in Glaucoma.

Using RPs improved the VF prediction compared with using sectoral RNFLTs.

Rates of Visual Field Loss in Primary Open-Angle Glaucoma and Primary Angle-Closure Glaucoma: Asymmetric Patterns.

POAG eyes showed a faster rate of VF loss in the superior hemifield compared to in the inferior hemifield, particularly in central and paracentral regions. This difference was not observed in PACG eyes.

Predicting Global Test-Retest Variability of Visual Fields in Glaucoma.

Inclusion of archetype VF loss patterns and TD values based on first VF improved the prediction of the global test-retest variability than using traditional global VF indices alone.

Estimating the Severity of Visual Field Damage From Retinal Nerve Fiber Layer Thickness Measurements With Artificial Intelligence.

The proposed ANN model estimated MD from RNFL measurements better than multivariable linear regression model, random forest, support vector regressor, and 1-D CNN models.

Distribution and Rates of Visual Field Loss across Different Disease Stages in Primary Open-Angle Glaucoma.

Our findings suggest that in POAG, VF damage is worse in the superior hemifield than in the inferior hemifield.

An Objective and Easy-to-Use Glaucoma Functional Severity Staging System Based on Artificial Intelligence.

We discovered that 4 severity levels based on MD thresholds of -2.2, -8.0, and -17.3 dB, provides the optimal number of severity stages based on unsupervised and supervised machine learning.

Artificial Intelligence and Glaucoma: Illuminating the Black Box.

Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility.

VF patterns of the worse eye are predictive of VF defects in the better eye.

Identifying Factors Associated With Fast Visual Field Progression in Patients With Ocular Hypertension Based on Unsupervised Machine Learning.

Unsupervised clustering can objectively identify OHT subtypes including those with fast VF worsening without human expert intervention.

Frequency of Visual Fields Needed to Detect Glaucoma Progression: A Computer Simulation Using Linear Mixed Effects Model.

Irregular visual field test frequency at relatively short intervals initially and longer intervals later on in the disease provided acceptable results in detecting glaucoma progression.

Pointwise and Region-Wise Course of Visual Field Loss in Patients With Glaucoma.

In the current study, nonlinear regression models showed a better fit compared to the linear regression models in tracking VF loss behavior.

Promise of Optical Coherence Tomography Angiography in Determining Progression of Glaucoma.

Reply to Comment on: Predicting Glaucoma Before Onset Using a Large Language Model Chatbot.

Impact of the Coronavirus Disease 2019 Pandemic on Surgical Volumes Among Fellowship-Trained Glaucoma Subspecialists.

Among glaucoma-trained surgeons, glaucoma surgeries experienced a greater volume loss than cataract surgeries.

Predicting Glaucoma before Onset Using Deep Learning.

Deep learning models can predict glaucoma development before disease onset with reasonable accuracy. Eyes with visual field abnormality but not glaucomatous optic neuropathy had a higher tendency to be missed by deep learning algorithms.

Optical Coherence Tomography Angiography Vessel Density in Healthy, Glaucoma Suspect, and Glaucoma Eyes.

Optical coherence tomography angiography vessel density had similar diagnostic accuracy to RNFL thickness measurements for differentiating between healthy and glaucoma eyes.

Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.

GEM-POP was significantly more sensitive to PGON than PoPLR and linear regression of MD and VFI in our sample, while providing localized progression information.