Advances in artificial intelligence models and algorithms in the field of optometry

There is an epidemic of myopia in East and Southeast Asia, with the prevalence of myopia in young adults around 80-90%, and an accompanying high prevalence of high myopia in young adults (10-20%). This may foreshadow an increase in low vision and blindness due to pathological myopia. These two epidemics are linked, since the increasingly early onset of myopia, combined with high progression rates, naturally generates an epidemic of high myopia, with high prevalences of "acquired" high myopia appearing around the age of 11-13. The major risk factors identified are intensive education, and limited time outdoors. The localization of the epidemic appears to be due to the high educational pressures and limited time outdoors in the region, rather than to genetically elevated sensitivity to these factors. Causality has been demonstrated in the case of time outdoors through randomized clinical trials in which increased time outdoors in schools has prevented the onset of myopia. In the case of educational pressures, evidence of causality comes from the high prevalence of myopia and high myopia in Jewish boys attending Orthodox schools in Israel compared to their sisters attending religious schools, and boys and girls attending secular schools. Combining increased time outdoors in schools, to slow the onset of myopia, with clinical methods for slowing myopic progression, should lead to the control of this epidemic, which would otherwise pose a major health challenge. Reforms to the organization of school systems to reduce intense early competition for accelerated learning pathways may also be important.

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.

Summary Background To contribute to the WHO initiative, VISION 2020: The Right to Sight, an assessment of global vision impairment in 2020 and temporal change is needed. We aimed to extensively update estimates of global vision loss burden, presenting estimates for 2020, temporal change over three decades between 1990–2020, and forecasts for 2050. Methods We did a systematic review and meta-analysis of population-based surveys of eye disease from January, 1980, to October, 2018. Only studies with samples representative of the population and with clearly defined visual acuity testing protocols were included. We fitted hierarchical models to estimate 2020 prevalence (with 95% uncertainty intervals [UIs]) of mild vision impairment (presenting visual acuity ≥6/18 and <6/12), moderate and severe vision impairment (<6/18 to 3/60), and blindness (<3/60 or less than 10° visual field around central fixation); and vision impairment from uncorrected presbyopia (presenting near vision