- AI-assisted Prediction of Healthy Aging and Alzheimer’s Disease Progression
Chaitanya Gupta, ProbiusDX Inc., Steven Arnold, Massachusetts General Hospital. This project will use Probius QES, a novel technology that measure molecular vibrations in blood plasma samples, to identify and differentiate healthy aging vs Alzheimer’s Disease progression in a well characterized cohort of 1000+ individuals from MassGen.
- Using AI to Repurpose Small Molecules to Target Amyloid-tau Interactions in AD
Jeremy Linsley, Operant BioPharma This project will use a proprietary robotics and artificial intelligence to identify targets and clinically-tested drugs that could be repurposed for Alzheimer’s disease by blocking the harmful interaction between Tau and Amyloid Precursor proteins.
- Behavioral Analytics is the New Medical Device
Rhoda Au, Boston University, Laura McIntosh, EmPowerYu. This project will use in-home multimodal sensors to detect changes in daily life activity patterns that indicate fluctuations in cognitive status. This passive, low-burden method that captures behavioral symptoms is a significant opportunity for understanding how we can individualize the monitoring and treatment of chronic diseases like Alzheimer’s Disease.
- Leveraging Digital Cognitive Rhythms to Detect ADRD Risk in Family Caregivers
Raeanne C Moore, UCSD, Yeonsu Song, UCLA. This project will develop and pilot machine learning algorithms to passively monitor cognitive fluctuations among family caregivers of persons living with dementia by analyzing their smartphone typing patterns and speech. By integrating KeyWise AI’s digital cognitive rhythm (“CogniRhythm”) score and ki:elements’ Speech Biomarker for Cognition (SB-C), this scalable, low-burden solution could identify and mitigate cognitive decline in this vulnerable population.
- TRIALCHAT: Leveraging LLMs to enhance AD/ADRD clinical trial participation
Tim K. Mackey, S-3 Research LLC, Joshua Yang, California State University, Fullerton. This project will aim to develop TrialChat, an AI-powered chatbot and clinical trial navigator designed to increase participation in Alzheimer’s disease and related dementias (ADRD) clinical trials by providing tailored education, personalized trial matching, and recruitment support for older adults and caregivers.
- AI-Driven Early Detection of AD Risk Using Speech Features
Marziye Eshghi, MGH Institute of Health Professions This project will leverage AI-driven analysis of remotely collected speech data to detect early signs of Alzheimer’s disease (AD) by linking speech acoustic and kinematic features to AD molecular pathologies. By validating these speech metrics and developing a predictive AI model, the study aims to enable earlier, more precise identification of individuals at risk for AD, facilitating timely intervention and personalized care.
- Measuring Heart Rate using Biomagnetism-based Wearable Devices
Longfei Shangguan, University of Pittsburgh This project will aim to develop an innovative wearable system that leverages biomagnetism to deliver more accurate heart rate and respiration monitoring across diverse skin tones.
Chaitanya Gupta, ProbiusDX Inc., Steven Arnold, Massachusetts General Hospital. This project will use Probius QES, a novel technology that measure molecular vibrations in blood plasma samples, to identify and differentiate healthy aging vs Alzheimer’s Disease progression in a well characterized cohort of 1000+ individuals from MassGen.
Jeremy Linsley, Operant BioPharma This project will use a proprietary robotics and artificial intelligence to identify targets and clinically-tested drugs that could be repurposed for Alzheimer’s disease by blocking the harmful interaction between Tau and Amyloid Precursor proteins.
Rhoda Au, Boston University, Laura McIntosh, EmPowerYu. This project will use in-home multimodal sensors to detect changes in daily life activity patterns that indicate fluctuations in cognitive status. This passive, low-burden method that captures behavioral symptoms is a significant opportunity for understanding how we can individualize the monitoring and treatment of chronic diseases like Alzheimer’s Disease.
Raeanne C Moore, UCSD, Yeonsu Song, UCLA. This project will develop and pilot machine learning algorithms to passively monitor cognitive fluctuations among family caregivers of persons living with dementia by analyzing their smartphone typing patterns and speech. By integrating KeyWise AI’s digital cognitive rhythm (“CogniRhythm”) score and ki:elements’ Speech Biomarker for Cognition (SB-C), this scalable, low-burden solution could identify and mitigate cognitive decline in this vulnerable population.
Tim K. Mackey, S-3 Research LLC, Joshua Yang, California State University, Fullerton. This project will aim to develop TrialChat, an AI-powered chatbot and clinical trial navigator designed to increase participation in Alzheimer’s disease and related dementias (ADRD) clinical trials by providing tailored education, personalized trial matching, and recruitment support for older adults and caregivers.
Marziye Eshghi, MGH Institute of Health Professions This project will leverage AI-driven analysis of remotely collected speech data to detect early signs of Alzheimer’s disease (AD) by linking speech acoustic and kinematic features to AD molecular pathologies. By validating these speech metrics and developing a predictive AI model, the study aims to enable earlier, more precise identification of individuals at risk for AD, facilitating timely intervention and personalized care.
Longfei Shangguan, University of Pittsburgh This project will aim to develop an innovative wearable system that leverages biomagnetism to deliver more accurate heart rate and respiration monitoring across diverse skin tones.