Martinyan Arthur

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.

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.