The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.

The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a “new normal”, the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.

PurposeTo compute choroidal vascularity index (CVI) using an image binarization tool on enhanced depth imaging (EDI)-optical coherence tomography (OCT) scans as a non-invasive optical tool to monitor progression in panuveitis and to investigate the utility of volumetric data from EDI-OCT scans using custom image analysis software.Materials and MethodsIn this retrospective cohort study, segmented EDI-OCT scans of both eyes in 19 patients with panuveitis were taken at baseline and at 3-month follow-up and were compared with EDI-OCT scans of normal eyes. Subfoveal choroidal area was segmented into luminal (LA) and stromal interstitial area (SA). Choroidal vascularity index (CVI) was defined as the proportion of LA to the total circumscribed subfoveal choroidal area (TCA).ResultsThe mean choroidal thickness was 265.5±100.1μm at baseline and 278.4±102.6μm at 3 months follow up (p = 0.06). There was no statistically significant difference in TCA between study and control eyes (p = 0.08). CVI in the control group was 66.9±1.5% at baseline and 66.4±1.5% at follow up. CVI was 74.1±4.7% at baseline and 69.4±4.8% at 3 months follow up for uveitic eyes (p<0.001). The % change in CVI was 6.2 ±3.8 (4.3 to 8.0) for uveitic eyes, which was significantly higher from % change in CVI for control eyes (0.7±1.1, 0.2 to 1.3, p<0.001).ConclusionThe study reports composite OCT-derived parameters and CVI as a possible novel tool in monitoring progression in panuveitis. CVI may be further validated in larger studies as a novel optical tool to quantify choroidal vascular status.