An AI-assisted screening platform within a multivariate framework for biomarkers of mild cognitive impairment due to Alzheimer's disease

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PROJECT SUMMARY Accumulating evidence indicates that every 65 seconds, someone develops Alzheimer's disease (AD) in the United States, and over 5.7 million Americans have the condition. Alzheimer's and other dementias will cost the nation $277 Billion by 2050. The major problem is that many people with cognitive impairment (CI) may not know they have it because dementia is underdiagnosed and underreported. There is a lack of low-cost and non- invasive screening instruments to automatically identify individuals at risk for CI with high accuracy. Therefore, considering the global and societal implications of the dementia epidemic, better strategies are needed to identify patients at risk for dementia. An eye health evaluation offers a unique perspective on the health of our eyes and our bodies. For example, visual observation of the retina as a diagnostic modality is already widely used to detect high blood pressure, diabetes, high cholesterol, and even brain tumors since a physician can see the optic nerve, which is part of the brain. Thus, an eye test may also be a potential solution to detect CI. Therefore, we aim to develop a low-cost, compact, and non-invasive neurotechnology supported by our multivariate biomarker methodology to address the pressing need for better diagnostics and surrogate markers of AD. We will further develop our novel fundus camera to develop a compact neurotechnology device that (1) enables non-invasive and portable retinal imaging with a new set of ring LED lights of specific narrow spectral bands, (2) integrates a light source for retinal stimulation and adds an optical design for recordings of the electrical activity of the retina, and (3) reduces the device cost >10 times compared to the current commercial devices based on unimodal technologies. These innovations will make our neurotechnology a rapid tool for assessing retinal function and structure easily usable by the non-expert operator, comfortable for the patient, and readily available in a wide range of healthcare settings globally. This project fills a critical technology gap in the field of AD diagnostics. While the number of screening tools using unimodal and expensive biomarkers continues to grow, these tools do not consider multivariate data generated during routine care in a collective and automated way. Thus, their diagnostic potential is limited. The development of our neurotechnology for detecting CI due to AD earlier, considering multifactorial variables and relevant biomarkers of ocular-brain abnormalities related to cognitive status, will allow earlier intervention and facilitate better management of the disease's primary cognitive symptoms.