The proposed study is designed to evaluate the performance of the ALTOIDAâ„¢ System as a tool to assist physicians in diagnosing Alzheimer's Disease (AD) in real-world clinical settings. The design of this study is guided by two overriding factors: 1) to optimize the performance of the ALTOIDAâ„¢ Neuro Motor Index (NMI) prognosis classifiers, the subjects making up the training sets must be well characterized as to their clinical diagnosis, and 2) all ALTOIDAâ„¢ tests must be performed and reproduced in real-world clinical settings. Although there is already a large body of peer-reviewed scientific literature demonstrating that certain digital biomarker patterns are associated with certain neurologic conditions, the utilization of such tools for the evaluation of neurologic disorders is still considered an emerging science and therefore in the investigational stage. Although this protocol will report on brain patterns of certain neurologic conditions such as cognitive impairment and Alzheimer's disease, based on patterns published in peer-reviewed journals, such findings are not considered stand alone or diagnostic per se and should always be considered by the primary physician in conjunction with the patient's clinical condition. These data should only be used as additional information to add to the primary physician's diagnostic impression.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Change in Diagnostic Area Under the Receiver Operating Characteristic Curve (ROC-AUC)
Timeframe: approximately 40 months follow up