Purpose To characterize the retinal phenotype of juvenile neuronal ceroid lipofuscinosis (JNCL), highlight delayed and mistaken diagnosis, and propose an algorithm for early identification. Design Retrospective case series. Participants Eight children (5 female) with JNCL. Methods Review of clinical notes, retinal imaging including fundus autofluorescence and OCT, electroretinography (ERG), and both microscopy and molecular genetic testing. Main Outcome Measurements Demographic data, signs and symptoms, visual acuity (VA), fundus autofluorescence and OCT findings, ERG phenotype, and microscopy/molecular genetics. Results Participants presented with rapid bilateral vision loss over 1 to 18 months, with mean VA deteriorating from 0.44 logarithm of the minimum angle of resolution (logMAR) (range, 0.20–1.78 logMAR) at baseline to 1.34 logMAR (0.30 logMAR - light perception) at last follow-up. Age of onset ranged from 3 to 7 years (mean, 5.3 years). The age at diagnosis of JNCL ranged from 7 to 10 years (mean, 8.3 years). Six children displayed eccentric fixation, and 6 children had cognitive or neurologic signs at the time of diagnosis (75%). Seven patients had bilateral bull’s-eye maculopathy at presentation. Coats-like exudative vasculopathy, not previously reported in JNCL, was observed in 1 patient. OCT imaging revealed near complete loss of outer retinal layers and marked atrophy of the nerve fiber and ganglion cell layers at the central macula. An electronegative ERG was present in 4 patients (50%), but with additional a-wave reduction, there was an undetectable ERG in the remaining 4 patients. Blood film microscopy revealed vacuolated lymphocytes, and electron microscopy showed lysosomal (fingerprint) inclusions in all 8 patients. Conclusions In a young child with bilateral rapidly progressive vision loss and macular disturbance, blood film microscopy to detect vacuolated lymphocytes is a rapid, readily accessible, and sensitive screening test for JNCL. Early suspicion of JNCL can be aided by detailed directed history and high-resolution retinal imaging, with subsequent targeted microscopy/genetic testing. Early diagnosis is critical to ensure appropriate management, counseling, support, and social care for children and their families. Furthermore, although potential therapies for this group of disorders are in early-phase clinical trial, realistic expectations are that successful intervention will be most effective when initiated at the earliest stage of disease.

Purpose To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. Methods A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. Results The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders’ compartmentalization was higher than the mean score for intra-grader group comparison. Conclusion The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders.

Optical coherence tomography (OCT) is a non-invasive medical imaging technology that is playing an increasing role in the routine assessment and management of patients with neuro-ophthalmic conditions. Its ability to characterise the optic nerve head, peripapillary retinal nerve fibre layer and cellular layers of the macula including the ganglion cell layer enables qualitative and quantitative assessment of optic nerve disease. In this review, we discuss technical features of OCT and OCT-based imaging techniques in the neuroophthalmic context, potential pitfalls to be aware of, and specific applications in more common neuro-ophthalmic conditions including demyelinating, inflammatory, ischaemic and compressive optic neuropathies, optic disc drusen and raised intracranial pressure. We also review emerging applications of OCT angiography within neuro-ophthalmology.