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Background/Purpose: To report a case of unilateral choroidal detachment and serous retinal detachment in a patient with a history of untreated sarcoidosis.Methods: Case report. The patient is a 67-year-old African American man with a history of nontreated sarcoidosis and prostate cancer. His prostate cancer was treated several years earlier with external beam radiation therapy. The patient presented with blurred visual acuity of 20/30 and floaters in the right eye. He was discovered to have several hypopigmented choroidal lesions, 360-degree choroidal detachment, and shallow serous retinal detachment in the right eye.Results: The patient was treated with subtenons kenalog and oral prednisone with subsequent improvement of vision and resolution of choroidal and retinal detachment.Conclusion: Ocular sarcoidosis can involve any part of the eye and its adnexal tissues and may cause uveitis, episcleritis, scleritis, eyelid abnormalities, conjunctival granuloma, optic neuropathy, lacrimal gland enlargement, and orbital inflammation. Most patients with ophthalmic sarcoidosis have evidence of systemic involvement at the time of the initial examination and have bilateral ocular presentation. We present here the unique case of a 67-year-old man with unilateral 360-degree choroidal detachment and serous retinal detachment as an ocular presentation of sarcoidosis.

PurposeTo assess the generalisability and performance of a deep learning classifier for automated detection of gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images.MethodsA convolutional neural network (CNN) model developed using data from the Chinese American Eye Study (CHES) was used to detect gonioscopic angle closure in AS-OCT images with reference gonioscopy grades provided by trained ophthalmologists. Independent test data were derived from the population-based CHES, a community-based clinic in Singapore, and a hospital-based clinic at the University of Southern California (USC). Classifier performance was evaluated with receiver operating characteristic curve and area under the receiver operating characteristic curve (AUC) metrics. Interexaminer agreement between the classifier and two human examiners at USC was calculated using Cohen’s kappa coefficients.ResultsThe classifier was tested using 640 images (311 open and 329 closed) from 127 Chinese Americans, 10 165 images (9595 open and 570 closed) from 1318 predominantly Chinese Singaporeans and 300 images (234 open and 66 closed) from 40 multiethnic USC patients. The classifier achieved similar performance in the CHES (AUC=0.917), Singapore (AUC=0.894) and USC (AUC=0.922) cohorts. Standardising the distribution of gonioscopy grades across cohorts produced similar AUC metrics (range 0.890–0.932). The agreement between the CNN classifier and two human examiners (Ҡ=0.700 and 0.704) approximated interexaminer agreement (Ҡ=0.693) in the USC cohort.ConclusionAn OCT-based deep learning classifier demonstrated consistent performance detecting gonioscopic angle closure across three independent patient populations. This automated method could aid ophthalmologists in the assessment of angle status in diverse patient populations.