Yang Hee Kyung

Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems.

Artificial intelligence-based deep learning algorithms for detecting optic disc diseases showed excellent performance in differentiating NGON and GON on color fundus photographs, necessitating further research for clinical application.

Glaucoma-like Parapapillary Choroidal Microvasculature Dropout in Patients with Compressive Optic Neuropathy.

OCT angiography of the peripapillary area showed retinal and choroidal microvasculature impairment in patients with both CON and OAG.

Comparison of Optic Nerve Head Microvasculature Between Normal-Tension Glaucoma and Nonarteritic Anterior Ischemic Optic Neuropathy.

Despite similar degrees of RNFL loss and VD decreases in the PR, VDs in the ONH differed between eyes with NTG and NAION, indicating different mechanisms of vascular impairment and ONH damage in each condition.

Differentiation of Nonarteritic Anterior Ischemic Optic Neuropathy from Normal Tension Glaucoma by Comparison of the Lamina Cribrosa.

LC morphology differed in eyes with NAION and NTG, despite a similar degree of RNFL damage.

Efficacy of automated computer-aided diagnosis of retinal nerve fibre layer defects in healthcare screening.

The new CAD system successfully detected RNFL defects during mass screening of fundus photographs in a large population who visited a healthcare centre.

Comparison of the Pattern of Retinal Ganglion Cell Damage Between Patients With Compressive and Glaucomatous Optic Neuropathies.

Distinct differences in the patterns of RGC damage in the macular and peripapillary areas were found between CON and GON.

Automatic computer-aided diagnosis of retinal nerve fiber layer defects using fundus photographs in optic neuropathy.

The proposed CAD system successfully detected RNFL defects in optic neuropathies. Thus, the proposed algorithm is useful for the detection of RNFL defects.