IMPORTANCE Quantitative volumetric measures of retinal disease in optical coherence tomography (OCT) scans are infeasible to perform owing to the time required for manual grading. Expert-level deep learning systems for automatic OCT segmentation have recently been developed. However, the potential clinical applicability of these systems is largely unknown.OBJECTIVE To evaluate a deep learning model for whole-volume segmentation of 4 clinically important pathological features and assess clinical applicability. DESIGN, SETTING, PARTICIPANTSThis diagnostic study used OCT data from 173 patients with a total of 15 558 B-scans, treated at Moorfields Eye Hospital. The data set included 2 common OCT devices and 2 macular conditions: wet age-related macular degeneration (107 scans) and diabetic macular edema (66 scans), covering the full range of severity, and from 3 points during treatment. Two expert graders performed pixel-level segmentations of intraretinal fluid, subretinal fluid, subretinal hyperreflective material, and pigment epithelial detachment, including all B-scans in each OCT volume, taking as long as 50 hours per scan. Quantitative evaluation of whole-volume model segmentations was performed. Qualitative evaluation of clinical applicability by 3 retinal experts was also conducted.

Background Acute posterior multifocal placoid pigment epitheliopathy (APMPPE) is a rare inflammatory eye disease, affecting the inner choroid and the outer retina. Recent advances in multimodal imaging have been important in the understanding of the pathophysiology of the disease, allowing a better characterization of the morphology of this condition. Methods Narrative review. Results In this review, a comprehensive overview of clinical features, imaging findings, treatment management, and long-term outcomes of patients with APMPPE will be provided. Conclusions Although APMPPE was originally believed to be a self-limited condition with a good prognosis, the disease can be recurrent and result in significant loss of vision function. Fundus imaging plays an important role in the diagnosis and management of the disease, allowing to evaluate response to treatment and onset of complications.