This technology is an automated screening algorithm that analyzes patterns in verbal communication for early stage diagnosis of Alzheimer’s disease and related cognitive impairments.
Mild cognitive impairment (MCI), Alzheimer’s disease (AD), and related early-stage dementias remain chronically underdiagnosed and untreated due to various factors such as the inability of patients to recognize early symptoms, limited availability of biomarkers, and clinicians’ having insufficient time and data to make a final diagnostic assessment. The currently available diagnostic assays remain invasive, costly and are unable to detect early stages of disease.
MCIADscreen utilizes a unique pipeline to model the three primary elements of speech: phonetic motor planning, semantic and syntactic language organization, and psycholinguistic features. This software processes audio recordings of patients’ verbal descriptions during a picture description test and uses various machine learning models to analyze the speech trace. By integrating acoustic, linguistic, and semantic features of speech, MCIADscreen provides an accurate and unbiased prediction for the likelihood of a patient developing symptoms of cognitive impairment.
This technology has been validated using the Dementia Bank English Pitt Corpus dataset, specifically through the “Cookie-Theft” picture description test.
Patent Pending
IR CU23077
Licensing Contact: Kristin Neuman
