Shi‐Min Hu

Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma.

Artifact correction for RNFLTs improves VF and progression prediction in glaucoma.

Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data.

The predicted 10-2 VF has the potential to improve glaucoma diagnosis.

Assessing the Accuracy of Artificial Intelligence-Generated Clinical Summaries From Ambulatory Glaucoma Subspecialty Clinical Encounters.

Although LLaMA 2 is not yet reliable as a standalone clinical tool, it shows promise to improve clinical communication.

Association Between Cup-to-Disc Ratio and Structural and Functional Damage Parameters in Glaucoma: Insights From Multiparametric Modeling.

CDR is subject to ceiling effects for glaucoma-related outcomes and poor at discriminating early glaucomatous damage. CDR values should be interpreted with care, particularly in screening settings.

Impact of Demographics on Regional Visual Field Loss and Deterioration in Glaucoma.

Blacks and non-English speakers have more severe VF loss, with superior hemifield being more affected and faster VF worsening.