This joint UK-Singapore collaborative R&D project aims to develop a new hand-held electronic ophthalmic medical device technology with embedded AI machine learning that can be used for remote and community eye-disease identification and monitoring.

Eye diseases are common, nearly 500,000 people in England have glaucoma. An estimated 2million people are living with significant sight-loss (UK) doubling to 4million by 2050. However, over 50% of sight-loss could be avoided a £28billion societal-economic cost burden. Vision-loss prevalence increases with age, including Age-related Macular Degeneration (AMD) a leading cause of blindness in adults.

NEED
False-positive referrals, existing waiting-lists, Covid-exacerbation and ageing populations requiring long-term eye-care is growing without the commensurate number of Ophthalmologists and Clinical resources to meet this demand-pressure on capacity - increasing risk of adverse-outcomes-for-patients (UK/Globally). This creates the need for an innovative ophthalmic-technology to reduce-demand and rapidly identify and monitor eye-disease in patients who need it most.

AMBITION
To deploy and validate an innovative ophthalmic eye-disease-assessment device enabling accurate real-time, measurable quantitative fundoscopy/pupillary-reactivity images. Tele-ophthalmology, remote-monitoring, recordable data enabling accurate diagnoses and monitoring of patients eye-disease. The machine-learning algorithms will allow nurses and health technicians to perform in community/primary eye-care settings stratification-screening of patients, which otherwise would require costly specialist NHS services) reducing false-positive referrals to secondary care, target treatment management of high-risk patients in specialist ophthalmology clinics and enable remote-monitoring of low-risk patients in the community. This is expected to be cost-saving.

FOCUS
This UK/Singaporean collaborative project will develop an innovative ophthalmic imaging medical device used by healthcare workers across community/primary/secondary care -- tele-ophthalmology enabled. Technology development - take and record patient unified eye-images Fundus/RAPD/Pupillary-response with interpretation embedded clinical AI algorithms. Health Economics/Commercialisation/Business Case demonstrating improved clinical patient pathways/reduced-costs/sight-loss/improved-patient-outcomes.

Innovation iReact innovative affordable, easy-to-use handheld eye-imaging device with embedded clinical algorithms enables accurate eye-disease prognoses enabling faster diagnosis, stratification monitoring and treatment. AI-machine learning of complex digital-images of the eye-physiology
enables real-time diagnostic interpretation, tele-ophthalmology enabled for remote monitoring. Faster treatment, improves patient outcomes especially in ageing populations or with complex comorbidities. Reduces costs of eye-care and sight-loss burden.