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 identify individuals at risk for CI with high accuracy automatically. 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. While early manifestations of numerous risk factors (e.g., diabetes, hypertension, and heart disease) have been found in the human retina, they may confound the first signs of CI. In healthcare, the complexity and rise in data volume have contributed to the remarkable worldwide interest of Artificial Intelligence (AI) applications in medicine. Therefore, we aim to provide a practical near-term risk assessment of CI through AI, by identifying and utilizing novel multivariate biomarkers (including eye markers) with a better discrimination power. In this Phase I STTR, iScreen 2 Prevent, LLC, the University of Miami, and the iCareHub, LLC, will develop an AI-based screening platform for early detection of CI due to AD. Our preliminary data show that multivariate eye biomarkers are related to cognitive status and can be used to discriminate mild CI patients from cognitively healthy subjects (age-matched (55+ years old), area under the receiving operating curve (AUROC)=0.90 (SE=0.050), p<0.001). However, multivariate biomarkers need to be combined at the point of screening to enhance the accuracy of predictions, and biomarker methodologies could be advanced using AI. We aim to integrate and optimize our eye screening framework (iScreen 2 Predict™) into a digital health platform (iCAREHub) that collects personalized, comprehensive clinical data at the point of care. We also aim to develop an AI-based model with the integrated multivariate markers and test the iScreen 2 Predict™ software's ability to discriminate patients with mild CI due to AD. 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 the routine care in a collective and automated way. Thus, their diagnostic potential is limited. The development of our software as a medical device (SaMD) 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.