Estimation of Visual Function Using Deep Learning From Ultra-Widefield Fundus Images of Eyes With Retinitis Pigmentosa.

Importance: There is no widespread effective treatment to halt the progression of retinitis pigmentosa. Consequently, adequate assessment and estimation of residual visual function are important clinically.

Objective: To examine whether deep learning can accurately estimate the visual function of patients with retinitis pigmentosa by using ultra-widefield fundus images obtained on concurrent visits.

Accuracy of a deep convolutional neural network in the detection of myopic macular diseases using swept-source optical coherence tomography.

This study examined and compared outcomes of deep learning (DL) in identifying swept-source optical coherence tomography (OCT) images without myopic macular lesions [i.e., no high myopia (nHM) vs.

Rare Association of Perivascular Granulomatous Lesions in a Patient with Acute Retinal Necrosis.

Purpose: The aim of this study was to examine sequential changes in perivascular granulomatous lesions with acute retinal necrosis (ARN).

Methods: A healthy 46-year-old Japanese woman, who developed floaters and pain in her left eye, underwent optical coherence tomography (OCT), fluorescein angiography, and routine ophthalmological examinations. Treatment-associated changes in perivascular granulomatous lesions were monitored using spectral-domain (SD)-OCT.