GaitIQ: Establishing a Digital Biomarker of Preclinical Alzheimer's Disease

早期发现临床前阿尔茨海默病（AD）之前的长潜伏期

Early detection of pre-clinical Alzheimer’s Disease (AD) during the long latent period prior to manifest dementia offers significant opportunities to advance the development of disease modifying interventions and effectively slow the disease’s progression. To achieve this objective, there is a critical need for new technologies that accelerate the development of biomarkers with high sensitivity for underlying AD pathology. A highly promising biomarker for preclinical AD is gait, as subtle gait changes have been correlated with elevated amyloid burden and cortical atrophy. While even simple measures of gait speed predict incident dementia in older adults, current research indicates that preclinical AD pathology is more precisely captured by a combination of 3D kinematic and spatio-temporal measures. A cost-effective mobile application that can be used in clinical trials and by healthcare personnel to capture these parameters efficiently, combined with a validated system to translate the measures to quantifiable AD risk in minutes, would result in a paradigm shift in availability of AD screening for at-risk individuals. GaitIQ™ is an innovative digital health startup company developing an online software-based product that employs computer vision and artificial intelligence (AI) to compute clinically accurate spatio-temporal and 3D kinematic data, from a simple video of a person walking. GaitIQTM collaborates with The Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases at UT Health San Antonio and Southwest Research Institute for this SBIR project. The expected outcome is that advanced kinematic/spatio-temporal measures of gait captured by the GaitIQTM system will reveal a sensitive and specific gait signature with high diagnostic accuracy for pre-clinical AD in a sample of Hispanic older adults. The project will develop and validate the capacity of GaitIQ™ to detect a digital gait biomarker signature that distinguishes between individuals with preclinical AD and controls. The final digital platform will be an easy-to-use, powerful tool to identify and monitor patients with pre-clinical AD using just an iPad/tablet to video their gait and submit it for analysis in the cloud by GaitIQTM sophisticated, proprietary analysis software.