SBIR-STTR Award

Alzheimer's Disease (AD) is one of the most common forms of dementia to occur in elderly populations,affecting over 30 million individuals worldwide. As the U.S. elderly population continues to increase, AD incidencerises as well, as there is no neuroprotective therapy or cure. Common symptoms include memory loss, cognitiveimpairment, disorientation, and psychiatric issues. Traditionally, diagnosis is achieved through a combination ofclinical criteria such as neurological examination, mental status tests & brain imaging. However, these strategiesare challenging for detection of early AD or patients with mild symptoms, specifically during the mild cognitiveimpairment (MCI) stage. Mental status tests & subjective journals, kept by patients or caregivers, can track ADprogression, but have low sensitivity and reliability. The most strongly established biomarkers for AD, includingamyloid beta, tau protein, & phosphorylated tau, are all obtained thru CSF requiring invasive lumbar puncture. The LifeBio-ALZ technology will provide a convenient and accessible, yet comprehensive digital biomarkerand analytics suite to detect & assess Alzheimer's progression. The platform will integrate a suite of assessmentdomains all seamlessly captured through a single, patient-centric app that engages users in natural video chatconversation via smart digital assistant. During brief, but regular sessions, an individual answers questionsfollowing a smart sequence to evaluate awareness, engagement, cognition, reaction time, speech patterns, &emotional state. The platform will record audio/video during the conversation. Type and timing of assessments,as well as specific questions will be adaptively modulated based on AD stage, personal demographics andprevious analytics to minimize user burden while still providing rich data for algorithms. Quantitative featuresacross multiple domains will be extracted from digital speech and eye movements, and then used as inputs toan AI engine to detect and assess Alzheimer's' disease progression. Data will be aggregated in secure cloudstorage with clinician access to dashboard visualization tools. Phase I will demonstrate core feasibility. Development will build on a strong tech foundation of an existingLifeBio platform to increase likelihood of success. Currently, LifeBio is deployed in several formats including web,phone, & mobile apps to record life histories of people reaching advanced age or facing life-threatening illnessesor memory loss. Natural language processing tools parse information into life stories shared by family or usedby staff to personalize engagement in care facilities. While the existing tech provides a base, significantenhancements will be executed in Phase I. More specifically, Phase I tasks will first update platform architectureto integrate novel data domains, build on smart sequenced multidimensional questions, and enhance patientworkflow interfaces. Once the enhanced app passes all technical verification testing, it will be deployed in a fielddata collection and usability study with wide ranging AD patient demographics and stages. Finally, collected datawill be used to build and validate an AI engine for detection and assessment of Alzheimer's progression. The Phase I objective is to design, develop and demonstrate feasibility of LifeBio-ALZ, an artificial intelligence (AI) driven, digital biomarker engine leveraging natural conversation to widely scale accessibility for early detection and assessment of Alzheimer's disease progression. Current biomarkers for early prediction of Alzheimer's include cerebral spinal fluid, circulatory biomarkers, blood based amyloid markers, inflammatory markers, & oxidative stress; which are expensive, time-consuming, & invasive. Therefore, a low-cost, patient- centered, & scalable tool with suite of digital biomarkers processed from natural conversation would have a significant impact on healthcare outcomes and costs. 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