Scalzo Fabien

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.

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.

Detection of Structural Glaucoma Progression with Deep Learning on Serial Optic Disc Photographs.

A deep learning model accurately detected glaucoma progression from serial optic disc photographs with high sensitivity. This offers a promising adjunctive tool for clinical decision-making in structural glaucoma monitoring.

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.