Insights into systemic disease through retinal imaging-based oculomics. Trans Vis Sci Tech. 2020;9(2):6, https://doi. org/10.1167/tvst.9.2.6 Among the most noteworthy developments in ophthalmology over the last decade has been the emergence of quantifiable high-resolution imaging modalities, which are typically non-invasive, rapid and widely available. Such imaging is of unquestionable utility in the assessment of ocular disease however evidence is also mounting for its role in identifying ocular biomarkers of systemic disease, which we term oculomics. In this review, we highlight our current understanding of how retinal morphology evolves in two leading causes of global morbidity and mortality, cardiovascular disease and dementia. Population-based analyses have demonstrated the predictive value of retinal microvascular indices, as measured through fundus photography, in screening for heart attack and stroke. Similarly, the association between the structure of the neurosensory retina and prevalent neurodegenerative disease, in particular Alzheimer's disease, is now well-established. Given the growing size and complexity of emerging multimodal datasets, modern artificial intelligence techniques, such as deep learning, may provide the optimal opportunity to further characterize these associations, enhance our understanding of eye-body relationships and secure novel scalable approaches to the risk stratification of chronic complex disorders of ageing.

A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches.

Purpose: To apply a deep learning algorithm for automated, objective, and comprehensive quantification of OCT scans to a large real-world dataset of eyes with neovascular age-related macular degeneration (AMD) and make the raw segmentation output data openly available for further research.Design: Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database.Participants: A total of 2473 first-treated eyes and 493 second-treated eyes that commenced therapy for neovascular AMD between June 2012 and June 2017.Methods: A deep learning algorithm was used to segment all baseline OCT scans. Volumes were calculated for segmented features such as neurosensory retina (NSR), drusen, intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperreflective material (SHRM), retinal pigment epithelium (RPE), hyperreflective foci (HRF), fibrovascular pigment epithelium detachment (fvPED), and serous PED (sPED). Analyses included comparisons between firstand second-treated eyes by visual acuity (VA) and race/ethnicity and correlations between volumes.Main Outcome Measures: Volumes of segmented features (mm 3 ) and central subfield thickness (CST) (mm).Results: In first-treated eyes, the majority had both IRF and SRF (54.7%). First-treated eyes had greater volumes for all segmented tissues, with the exception of drusen, which was greater in second-treated eyes. In first-treated eyes, older age was associated with lower volumes for RPE, SRF, NSR, and sPED; in second-treated eyes, older age was associated with lower volumes of NSR, RPE, sPED, fvPED, and SRF. Eyes from Black individuals had higher SRF, RPE, and serous PED volumes compared with other ethnic groups. Greater volumes of the majority of features were associated with worse VA.Conclusions: We report the results of large-scale automated quantification of a novel range of baseline features in neovascular AMD. Major differences between firstand second-treated eyes, with increasing age, and between ethnicities are highlighted. In the coming years, enhanced, automated OCT segmentation may assist personalization of real-world care and the detection of novel structureefunction correlations. These data will be made publicly available for replication and future investigation by the AMD research community.

BackgroundCases of patients with primary intraocular lymphoma (PIOL) were retrospectively analyzed to describe the longitudinal intra-retinal morphological changes in PIOL as visualized on images obtained by spectral domain optical coherence tomography (SD-OCT).ResultsIn a retrospective case series, Heidelberg Spectralis SD-OCT images obtained in the longitudinal evaluation of patients with biopsy-proven PIOL were analyzed and assessed. The images were graded for the presence of macular edema (ME), pigment epithelial detachment (PED), subretinal fluid (SRF), and hyperreflective signals. SD-OCT scans of five eyes from five patients were assessed. Patients showed signs of inflammation, such as ME and SRF, which were resolved with treatments in some cases. Hyperreflective signals were found in all eyes in the form of nodules or bands across the retina, with the highest frequency of appearance in the ganglion cell layer, inner plexiform layer, photoreceptor layer, and retinal pigment epithelium; such signals increased with the progression of PIOL.ConclusionSD-OCT may be employed to monitor the progression of PIOL. Hyperreflective signals on OCT may correspond with increase in disease activities, along with other findings such as ME, PED, and SRF.