Detection of macular atrophy in age-related macular degeneration aided by artificial intelligence.

INTRODUCTION: Age-related macular degeneration (AMD) is a leading cause of irreversible visual impairment worldwide. The endpoint of AMD, both in its dry or wet form, is macular atrophy (MA) which is characterized by the permanent loss of the RPE and overlying photoreceptors either in dry AMD or in wet AMD. A recognized unmet need in AMD is the early detection of MA development. AREAS COVERED: Artificial Intelligence (AI) has demonstrated great impact in detection of retinal diseases, especially with its robust ability to analyze big data afforded by ophthalmic imaging modalities, such as color fundus photography (CFP), fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT). Among these, OCT has been shown to have great promise in identifying early MA using the new criteria in 2018. EXPERT OPINION: There are few studies in which AI-OCT methods have been used to identify MA; however, results are very promising when compared to other imaging modalities. In this paper, we review the development and advances of ophthalmic imaging modalities and their combination with AI technology to detect MA in AMD. In addition, we emphasize the application of AI-OCT as an objective, cost-effective tool for the early detection and monitoring of the progression of MA in AMD.

Quantitative brain-derived neurotrophic factor lateral flow assay for point-of-care detection of glaucoma.

Glaucoma, a ruinous group of eye diseases with progressive degeneration of the optic nerve and vision loss, is the leading cause of irreversible blindness. Accurate and timely diagnosis of glaucoma is critical to promote secondary prevention and early disease-modifying therapies. Reliable, cheap, and rapid tests for measuring disease activities are highly required. Brain-derived neurotrophic factor (BDNF) plays an important role in maintaining the function and survival of the central nervous system. Decreased BDNF levels in tear fluid can be seen in glaucoma patients, which indicates that BDNF can be regarded as a novel biomarker for glaucoma. Conventional ELISA is the standard method to measure the BDNF level, but the multi-step operation and strict storage conditions limit its usage in point-of-care settings. Herein, a one-step and a portable glaucoma detection method was developed based on the lateral flow assay (LFA) to quantify the BDNF concentration in artificial tear fluids. The results of the LFA were analyzed by using a portable and low-cost system consisting of a smartphone camera and a dark readout box fabricated by 3D printing. The concentration of BDNF was quantified by analyzing the colorimetric intensity of the test line and the control line. This assay yields reliable quantitative results from 25 to 300 pg mL-1 with an experimental detection limit of 14.12 pg mL-1. The LFA shows a high selectivity for BDNF and high stability in different pH environments. It can be readily adapted for sensitive and quantitative testing of BDNF in a point-of-care setting. The BDNF LFA strip shows it has great potential to be used in early glaucoma detection.