Kanamoto Takashi

Using Deep Learning and Transfer Learning to Accurately Diagnose Early-Onset Glaucoma From Macular Optical Coherence Tomography Images.

A DL model for glaucoma using spectral-domain OCT offers a substantive increase in diagnostic performance.

Predicting the Glaucomatous Central 10-Degree Visual Field From Optical Coherence Tomography Using Deep Learning and Tensor Regression.

The Humphrey 10-2 VF can be predicted from OCT-measured retinal layer thicknesses using deep learning and tensor regression.

Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field.

The performance of a DL model to predict 10-2 VF from macular OCT was improved by the correction with HFA 24-2/30-2.

Comparing Structure-Function Relationships Based on Drasdo's and Sjöstrand's Retinal Ganglion Cell Displacement Models.

Structure-function relationships evaluated based on both the Drasdo and Sjöstrand models significantly improved around the fovea, particularly when using the Drasdo model. This was not the case in other areas.

Improving Visual Field Trend Analysis with OCT and Deeply Regularized Latent-Space Linear Regression.

It is useful to include OCT measurements when predicting future VF progression in glaucoma patients, especially with short VF series.