ALZ-Stage: Artificial intelligence driven technology with adaptive suite of personalized tests to detect and determine progressive stage of Alzheimers disease

Alzheimerandapos s DiseaseADis one of the most common forms of dementia to occur in elderly populations around the globecurrently affecting overmillion people worldwideAs the U Selderly population continues to increasethe incidence of AD rises as wellas there is no known neuroprotective therapy or cureThe most common symptoms include memory losscognitive impairmentdisorientationand psychiatric issuesThe initial diagnosis is achieved through a combination of clinical criteria including a neurological examinationmental status tests and brain imagingHoweverthese strategies are challenging for detection of early AD or patients with mild symptomsspecifically during the mild cognitive impairmentMCIstageMental status tests and subjective journalskept by patients or caregiversare often used to track AD progressionbut have low sensitivity and reliability for clinical trialsThe most strongly established biomarkers for ADincluding amyloid betatau proteinand phosphorylated tauare all obtained thru CSF requiring invasive lumbar punctureALZ Stage technology will provide a convenient and accessibleyet comprehensive analytics suite to detect and stage Alzheimerandapos s disease progressionThe platform will integrate a progressive suite of diagnostic tests using a variety of biologicalneurologicaland behavioral platformssubjective and objective inputsand active and passive test componentsThe test battery may include blood sampleurine sampleswearable sensorsmobile phone sensors and behavioral trackingThe implementation strategy will progress from lower costmore readily accessible and passive monitoring devices to more expensiveinvasive and burdensome test as likelihood or ALZ stage increasesUnique patient demographics may impact sensitivityFor exampleAD incidence in women is twice as high as menAdditionallybased on socio economic or geographic disparities some populations may only have access to certain types of test equipment or supplies compared to othersThereforeALZ Stage will use AI algorithms not only to detect and stage ADbut will also determine the specific test battery to use based on availability of test options and patient demographicsThe Phase I effort will target the highest risk project component which includes core algorithm development and verificationWe will increase likelihood of project success by building on our existing framework that has shown strong feasibility for adaptively predicting test batteries to screen patients for Parkinsonandapos s diseaseThe work will be completed through strong collaboration with our expert technical team at Elder Research and our strong clinical consultantsMore specificallyPhase I tasks will focus on developing an array of biomarker test suites for ADcollecting data from a wide range of AD subjects and controls with widely varying demographicsand using that data to build a two layer intelligent algorithm that can determine the most appropriate test battery for a subject and also accurately detect and stage AD during the MCI stage The objective is to designdevelop and demonstrate feasibility of ALZ Stagean artificial intelligence driven technology that utilizes an adaptive suite of personalized diagnostic tests to both detect and determine the progressive stage of MCI in Alzheimerandapos s diseaseWhile more thanmillion Americans are living with Alzheimerandapos searly and accurate diagnosis can significantly improve outcomes and could save up to $trillion in healthcare and long term costsThereforea low costpersonalizedand widely accessible test suite to detect and stage AD progression would have a significant positive impact on healthcare outcomes and costsas well as neuroprotective trials aimed at stopping or slowing disease progression