Besharati Sajad

Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning.

DL model predicted VF progression with clinically relevant accuracy using baseline RNFL thickness and serial ODPs and can be implemented as a clinical tool after further validation.

Enhancing Detection of Glaucoma Progression: Utility of 24-2 Visual Field Central Points vs. 10-2 Visual Fields.

MD12 RoC and detection rates have a low level of agreement with those of 10-2 and hence do not replace the need for 10-2 VF MD to monitor central damage.

Outcomes of Trabeculectomy With Mitomycin C in Patients of Hispanic vs European Descent.

In this study, Hispanic descent was associated with higher failure rate after initial trabeculectomy with adjunctive MMC compared with European descent.

Association of Blood Pressure and Retinal Nerve Fiber Layer Rates of Thinning in Patients with Moderate to Advanced Glaucoma.

Low BP and higher IOP at baseline predicted faster (worse) RNFL RoCs in glaucoma patients with central damage or moderate to advanced disease.

Detecting Fast Progressors: Comparing a Bayesian Longitudinal Model to Linear Regression for Detecting Structural Changes in Glaucoma.

The Bayesian HSL model improves the estimation efficiency of local GCC rates of change regardless of underlying true rates of change, particularly in fast progressors.

Retinal Nerve Fiber Layer Rates of Change: Comparison of 2 OCT Devices.

Spectralis OCT rates of RNFL change were faster compared to those from Cirrus OCT.

Comparison of Retinal Nerve Fiber Layer and Ganglion Cell Complex Rates of Change in Patients With Moderate to Advanced Glaucoma.

Both GCC and RNFL measures can detect structural progression in glaucoma patients with central damage or moderate to advanced glaucoma. The clinical utility of RNFL imaging decreases with worsening severity of glaucoma.

An Artificial Intelligence-Based Prognostic Model for Prediction of Functional Glaucoma Progression From Clinical and Structural Data.

Our newly designed deep learning model can combine baseline demographic and clinical data with widely available structural measurements and provide clinically relevant information for the prediction of glaucoma progression.

A Bayesian Hierarchical Longitudinal Model for Estimation of Central Visual Field Rates of Change in Glaucoma.

When baseline pointwise sensitivity is 5 to 20 dB, residual variability is very large, substantially reducing the ability to detect glaucoma progression.

Prediction of visual field progression with serial optic disc photographs using deep learning.

A deep learning model can predict subsequent glaucoma progression from longitudinal ODPs with clinically relevant accuracy. This model may be implemented, after validation, for predicting glaucoma progression in the clinical setting.