Unberath Mathias

Predicting Visual Field Worsening with Longitudinal OCT Data Using a Gated Transformer Network.

Gated transformer network models trained with OCT data may be used to identify VF worsening.

Comparing the Accuracy of Peripapillary OCT Scans and Visual Fields to Detect Glaucoma Worsening.

More frequent OCT scans and VF tests are needed to improve the accuracy of diagnosing glaucoma worsening.

Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data.

Deep learning models can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course.

Evidence-Based Guidelines for the Number of Peripapillary OCT Scans Needed to Detect Glaucoma Worsening.

To diagnose RNFL worsening more accurately, the number of OCT scans must be increased compared with current clinical practice. A clustered measurement strategy reduces the number of scans required compared with evenly spacing measurements.

The Effect of Achieving Target Intraocular Pressure on Visual Field Worsening.

In treated patients, failing to achieve target IOP was associated with more rapid VF worsening. Eyes with moderate glaucoma experienced the greatest VF worsening from failing to achieve target IOP.

Improving Visual Field Forecasting by Correcting for the Effects of Poor Visual Field Reliability.

Including all VFs in the trend estimation has more predictive power for future reliable VFs than excluding unreliable VFs. Correcting for VF reliability further improves model accuracy.

Opportunities for Improving Glaucoma Clinical Trials via Deep Learning-Based Identification of Patients with Low Visual Field Variability.

Deep learning models can forecast eyes with low VF variability using data from a single baseline clinical visit.