We read with interest Dewing et al.’s paper on the disparity between funding for eye research versus the significant cost of sight-loss in the UK [1]. This article highlights the comparatively low level of ophthalmic research funding in the UK, when compared to cardiovascular disease and cancer research, despite the significant economic impacts of visual impairment [1]. Preventable vision loss imposes significant costs on health, public and private funds, with the economic impact of blindness and visual impairment in the United Kingdom estimated to be over £15.8 billion in 2013 [2]. With aging populations, the prevention of avoidable blindness will become an even greater challenge and novel, more efficient methods of vision screening need to be implemented.
The advent of machine learning, particularly deep learning, has revolutionized the field of medicine. The integration of machine learning with visual assessment technology is an emerging method to reduce preventable blindness and ophthalmic care inequalities. Further research into improving vision screening is essential, as even in England, a significant proportion of cases of treatable disorders including age-related macular degeneration remain unreported [3]. Current home screening for neovascular AMD involves traditional paper based Amsler grids which involves subjective evaluation of vision combined with serial clinic follow-up. With increasing aging populations worldwide, screening populations for preventable causes of blindness will become even more challenging. Although our current research centers on the development of a visual assessment system with machine learning algorithms to assess subtle changes in astronaut vision during spaceflight, we believe that the technology has potential terrestrial applications as well [4, 5]. This technology aims to detect and more objectively quantify, using a single device, the many parameters of subjective visual function (e.g., visual acuity, color vision, contrast sensitivity, visual field testing, and assessment for metamorphopsia).
Low-cost visual assessment with VR headsets combined with AI may be able to provide new possibilities for ophthalmic screening in these underserved areas. These headsets could be made available to communities for synchronous or asynchronous evaluations to improve screening. We believe that both machine learning and VR visual assessment technology are promising methods to help reduce preventable blindness worldwide in the coming future.
Author contributions
EW—Conceptualization, Writing. JO—Conceptualization, Writing. PP—Review, Intellectual Support. SK—Review, Intellectual Support. NZ—Review, Intellectual Support. AT—Review, Intellectual Support. AGL—Review, Intellectual Support.
Funding
NASA Grant [80NSSC20K183]: A Non-intrusive Ocular Monitoring Framework to Model Ocular Structure and Functional Changes due to Long-term Spaceflight.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
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