Review of Retinal Imaging Modalities for Hydroxychloroquine Retinopathy.

This review provides an overview of conventional and novel retinal imaging modalities for hydroxychloroquine (HCQ) retinopathy. HCQ retinopathy is a form of toxic retinopathy resulting from HCQ use for a variety of autoimmune diseases, such as rheumatoid arthritis and systemic lupus erythematosus. Each imaging modality detects a different aspect of HCQ retinopathy and shows a unique complement of structural changes. Conventionally, spectral-domain optical coherence tomography (SD-OCT), which shows loss or attenuation of the outer retina and/or retinal pigment epithelium-Bruch's membrane complex, and fundus autofluorescence (FAF), which shows parafoveal or pericentral abnormalities, are used to assess HCQ retinopathy. Additionally, several variations of OCT (retinal and choroidal thickness measurements, choroidal vascularity index, widefield OCT, en face imaging, minimum intensity analysis, and artificial intelligence techniques) and FAF techniques (quantitative FAF, near-infrared FAF, fluorescence lifetime imaging ophthalmoscopy, and widefield FAF) have been applied to assess HCQ retinopathy. Other novel retinal imaging techniques that are being studied for early detection of HCQ retinopathy include OCT angiography, multicolour imaging, adaptive optics, and retromode imaging, although further testing is required for validation.

Optical Coherence Tomography Angiography in Retinal Vascular Disorders.

Traditionally, abnormalities of the retinal vasculature and perfusion in retinal vascular disorders, such as diabetic retinopathy and retinal vascular occlusions, have been visualized with dye-based fluorescein angiography (FA). Optical coherence tomography angiography (OCTA) is a newer, alternative modality for imaging the retinal vasculature, which has some advantages over FA, such as its dye-free, non-invasive nature, and depth resolution. The depth resolution of OCTA allows for characterization of the retinal microvasculature in distinct anatomic layers, and commercial OCTA platforms also provide automated quantitative vascular and perfusion metrics. Quantitative and qualitative OCTA analysis in various retinal vascular disorders has facilitated the detection of pre-clinical vascular changes, greater understanding of known clinical signs, and the development of imaging biomarkers to prognosticate and guide treatment. With further technological improvements, such as a greater field of view and better image quality processing algorithms, it is likely that OCTA will play an integral role in the study and management of retinal vascular disorders. Artificial intelligence methods-in particular, deep learning-show promise in refining the insights to be gained from the use of OCTA in retinal vascular disorders. This review aims to summarize the current literature on this imaging modality in relation to common retinal vascular disorders.

Updates in deep learning research in ophthalmology.

Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.