Cohort 3

Awardee Organization(s): DreamFace Technologies LLC
Principal Investigator(s): Mohammad H. Mahoor, PhD
Official Project Title: Building Deep Digital Twins for Prediction of AD/ADR/MCI in Older Adults
AITC Partner: PennAITech
Website(s): https://dreamfacetech.com/

The Alzheimer’s Association predicts that the number of Americans aged 65 and older with Alzheimer’s disease-related dementia (ADRD) will reach over 12 million people by 2050. ADRD often stars with mild cognitive impairment (MCI), which is characterized by challenges in memory, language, and thinking skills. Early MCI detection is vital for identifying those at risk of dementia, offering support, advice, and ongoing monitoring. Currently, older adults with MCI are diagnosed clinically; however, their daily challenges are often not noticeable to those whom they encounter irregularly. Artificial Intelligence (AI) holds promise for early cognitive impairment detection, with many AI studies focusing on expensive clinical assessments and medical scans like positron emission tomography (PET) and MRI. There is a pressing need for additional research to advance innovative, cost-effective, and accessible approaches for early detection and prediction of AD and MCI. Human digital twins are at the forefront of aging and longevity research, aiming to create personalized AI models that comprehensively simulate an individual’s behavioral, biological, physical, mental, and socio-emotional attributes using health and medical records. These models hold the potential to revolutionize our understanding, prediction, and management of the aging process, offering personalized healthcare solutions. This pilot project aims to investigate AI techniques that leverage multi-modal audio-visual data, along with other available data modalities, to develop human digital twins for research in aging and, more specifically, for predicting MCI and the early onset of AD/ADRD. We design and implement a Deep Digital Twins (DDT) model using Conditional Variational Autoencoders (CVAEs) suitable for heterogeneous multi-modal data including speech, transcribed speech, and facial videos. We then evaluate the efficacy of the proposed model using publicly available datasets such as the I-CONECT and ADReSS datasets, which contain multi-modal data and other metadata suitable for our project. We hypothesize that DDTs trained using multi-modal comprehensive data can predict MCI/AD with high fidelity and accuracy compared to uni-modal data. We compare our proposed DDT with state-of-the-art models in the literature. We assess the models’ performance, taking into account the impact of diverse data to ensure they remain unbiased. The expected outcome of this research are knowledge and prototyped Deep Digital Twins capable of assessing and predicting MCI/AD conditions in older adults. It is expected that the DDTs generate the longitudinal trajectories sampled from the data as well as predict the subject’s future condition.

Awardee Organization(s): University of Tennessee, Knoxville
Principal Investigator(s): Xiaopeng Zhao, PhD
Official Project Title: MUSICARE-VR: Music Intervention with Virtual Reality for Alzheimer’s Care
AITC Partner: PennAITech
Website(s): https://mabe.utk.edu/

MUSICARE-VR is an innovative system that combines the benefits of music intervention with the connective power of virtual reality to improve the well-being of people with Alzheimer’s disease and related dementia (PwADRD). By providing engaging music sessions in a virtual environment, MUSICARE-VR aims to enhance cognitive function, physical activity, emotional positivity, and social connectedness among PwADRD, especially those living in isolation. The system will be developed using cutting-edge virtual reality technologies. PwADRD will participate in personalized, interactive music activities led by skilled music therapists, fostering a sense of achievement and encouraging repeated engagement. A key feature of MUSICARE-VR is the integration of artificial intelligence (AI), which will adapt music interventions in real-time based on users’ emotional and physiological responses, ensuring an engaging and effective experience. AI-powered virtual agents will also join the sessions, enhancing social interactions and overall engagement. To ensure the system’s success, MUSICARE-VR will be developed through a user-centered, iterative design process. The feasibility and acceptance of the system will be evaluated among PwADRD and their caregivers, with a focus on usability, engagement metrics, and participant feedback. By combining music intervention, virtual reality, and AI, MUSICARE-VR promises to be a groundbreaking tool for improving the quality of life of PwADRD and their caregivers.

Awardee Organization(s): PyrAmes Inc.
Principal Investigator(s): Xina Quan, PhD
Official Project Title: Improved Algorithms for Wearable Blood Pressure Monitoring for Older Adults
AITC Partner: PennAITech
Website(s): https://pyrameshealth.com/

Older adults with high blood pressure (BP) are at increased risk of severe health concerns, e.g. heart disease, congestive heart failure, ischemic stroke, cerebral hemorrhage, vascular dementia and Alzheimer’s disease. Frequent measurements improve BP control, leading to improved outcomes.
A significant barrier to BP control is obtaining sufficient measurements for effective management. Continuous monitoring with invasive arterial lines is limited to critical care facilities. Periodic cuff measurements through ambulatory BP monitoring provide an indication of BP variation, but devices are cumbersome and uncomfortable, leading to incorrect or insufficient usage.
A more convenient, cost-effective BP monitoring method providing passive measurements and actionable information potentially leads to reduced risk from cardiovascular disease. PyrAmes has developed a comfortable, easy-to-use sensor band to monitor BP, enabling long-term, personalized BP management. It is soft, flexible, and lightweight, and has been validated for use with patients with fragile skin. Our innovative approach uses patented capacitive sensors to capture pulse waveform data, which is processed on a connected mobile device with neural networks to accurately determine BP values and provide detailed information about cardiovascular health.
Our first device, Boppli®, was FDA-cleared in 2023 for continuously monitoring the BP of critically-ill neonates. Our adult monitor uses identical sensors and validated software infrastructure and has shown initial feasibility. This project accelerates development for the older adult population, leading to FDA clearance and commercialization.
Our technology has the potential to become as ubiquitous for BP measurement as pulse oximeters are today, due to its accuracy, convenience, and ease of use.

Awardee Organization(s): University of Missouri
Principal Investigator(s): Trent M. Guess, PhD
Official Project Title: Motor Function Assessment for Mild Cognitive Impairment, Frailty, and Fall Risk
AITC Partner: PennAITech
Website(s):

Homepage

https://mizzoumotioncenter.com/

Fall risk, mild cognitive impairment (MCI), and frailty are three interrelated health conditions that diminish quality of life for older adults and put them at higher risk for adverse outcomes, including hospitalization, disability, and death. A common characteristic shared by these conditions is a decline in motor function, most often manifested by degradation in balance and gait performance. Comprehensive early detection of motor declines may offer our best chance of addressing these geriatric conditions. While there is growing interest in using sensors to measure movement and balance, currently available technologies are prohibitively expensive or do not capture multiple aspects of movement. As a solution, we have developed the Mizzou Point-of-Care Assessment System (MPASS), which integrates measurements from multiple sensors to provide an objective, comprehensive dataset of human movement and cognitive performance. The total cost of the testing platform is under $1,500 and MPASS motor function assessments typically take less than 15 minutes. Our goal is to integrate the MPASS with artificial intelligence (AI) approaches to translate the system into a clinically effective tool that quickly, affordably, and accurately assesses risk for falling, MCI, and frailty, in real-world clinical and community settings. Specifically, we will collect data on MPASS motor function, cognitive testing, fall history, and frailty for 30 persons with MCI and 50 community dwelling adults. We will then employ AI to develop prediction algorithms that distinguish persons with MCI, fall risk, and frailty. Finally, we will develop clinically usable outputs based on the prediction algorithms.

Awardee Organization(s): University of Georgia
Principal Investigator(s): Soheyla Amirian, PhD
Official Project Title: AI-Powered Web Application to Analyze Knee Joint Space for Aging Population
AITC Partner: PennAITech
Website(s): https://engineering.uga.edu/team_member/soheyla-amirian/

Osteoarthritis (OA) stands as a prevailing chronic joint affliction, affecting millions of individuals globally, particularly those aged 65 or older. We propose the development of an innovative AI-powered web application, tailored to facilitate the monitoring and analysis of knee joint space, and by extension, the progression of knee OA. This web application will serve as a beacon of hope for aging individuals suffering from the burden of knee OA. By building, training, and validating deep learning computer vision algorithm, we aim to empower patients, their caregivers, and healthcare providers with an intuitive and cost-effective solution. Our overarching goal is thus to provide a platform that allows for the quantitative and longitudinal assessment of knee joint space, thereby enhancing our understanding of the degeneration process in the aging knee. Beyond the technical intricacies, our mission is deeply rooted in delivering a solution that is accessible to those who need it the most. This technology will bridge geographical distances, transcending traditional healthcare limitations and opening new avenues for remote patient care. As we delve into the specific aims of this project, it is vital to underscore the potential impact it holds for individuals afflicted by knee OA, the individuals who care for them, and the healthcare professionals committed to their well-being. The specific aims are: (1) To develop an AI-powered web application to quantitatively assess and analyze knee joint space using only plain radiographs.Osteoarthritis (OA) stands as a prevailing chronic joint affliction, affecting millions of individuals globally, particularly those aged 65 or older. We propose the development of an innovative AI-powered web application, tailored to facilitate the monitoring and analysis of knee joint space, and by extension, the progression of knee OA. This web application will serve as a beacon of hope for aging individuals suffering from the burden of knee OA. By building, training, and validating deep learning computer vision algorithm, we aim to empower patients, their caregivers, and healthcare providers with an intuitive and cost-effective solution. Our overarching goal is thus to provide a platform that allows for the quantitative and longitudinal assessment of knee joint space, thereby enhancing our understanding of the degeneration process in the aging knee. Beyond the technical intricacies, our mission is deeply rooted in delivering a solution that is accessible to those who need it the most. This technology will bridge geographical distances, transcending traditional healthcare limitations and opening new avenues for remote patient care. As we delve into the specific aims of this project, it is vital to underscore the potential impact it holds for individuals afflicted by knee OA, the individuals who care for them, and the healthcare professionals committed to their well-being. The specific aims are: (1) To develop an AI-powered web application to quantitatively assess and analyze knee joint space using only plain radiographs. (2) To establish a prospective adult cohort with knee OA to clinically validate the AI-powered web application.

Awardee Organization(s): Drexel University
Principal Investigator(s): Hualou Liang, PhD
Official Project Title: Detecting Cognitive Impairment Using Large Language Models from Speech
AITC Partner: PennAITech
Website(s): https://drexel.edu/biomed/

Awardee Organization(s): Palliative and Advanced Illness Research (PAIR) Center
Principal Investigator(s): Emily Moin, MD, MBE
Official Project Title: Determinants of Access to and Outcomes Following Specialized Palliative Care for Patients with ADRD
AITC Partner: PennAITech
Website(s): https://pair.upenn.edu/

As a supplement to the PennAITech program, we will link electronic health record data from all patients at the University of Pennsylvania Health System (Penn Medicine) with inpatient encounters from 2017-2023 to claims data from the Centers for Medicare and Medicaid Services (CMS) from 2018-2024. The central goal of creating these linkages is to identify determinants of access to and outcomes following specialist palliative care (SPC) for patients with ADRD and other serious illnesses. Engaging SPC clinicians in the longitudinal management of patients with ADRD and other serious illnesses is a key priority, yet patients with ADRD less commonly receive SPC than do patients with cancer and other illnesses, and more commonly experience hospitalizations and inadequate symptom control. To surmount these inequities in care delivery requires filling key knowledge gaps related to the extents to which patients with ADRD experience novel and more holistic patient- centered outcomes than have commonly been measured, and whether patients at risk for poor outcomes can be reliably identified so as to target SPC resources toward them.
We will achieve this central goal and address these knowledge gaps through completion of three specific aims. First, we will quantify differences in SPC consultation among Medicare and Medicaid beneficiaries with vs. without AD/ADRD admitted to Penn Medicine hospitals. We will first assess SPC access using the gold-standard approach we have pioneered of measuring signed SPC notes in the electronic health record (EHR), and second using the proportions with Z51.5 billing codes (“encounter for palliative care”) in CMS data. While this claims-based approach is commonly used due to its efficiency, there are many reasons to believe it may not possess favorable operating characteristics, thereby yielding biased conclusions when used as a measure of SPC receipt. Comparing its sensitivity, specificity, calibration, and other measures to our gold standard will elucidate this approach’s utility overall and specifically among patients with ADRD.
Second, we measure changes in calculating a key patient-centered outcome – hospital-free days (HFDs), or days alive and living outside a hospital through 6 months of follow-up – using only EHR data vs. supplementing with CMS data among patients with ADRD and other serious illnesses. We hypothesize that adding CMS data to EHR data will produce more robust and accurate quantification of this critical outcome relative to either method alone. Third, we will build an Artificial Intelligence (AI) model to predict which patients with (1) ADRD and (2) all serious illnesses are at risk for low numbers of HFDs, thereby guiding the allocation of SPC services in practice enabling prognostic enrichment of patients with ADRD in future trials of palliative care interventions using this endpoint.

Awardee Organization(s): Pennsylvania State University
Principal Investigator(s): Rui Zhang, PhD | Marie Boltz, PhD, GNP-BC
Official Project Title: Task-Oriented Multimodal Conversational AI for Assisting Seniors with Daily Tasks
AITC Partner: PennAITech
Website(s):
https://www.eecs.psu.edu/
https://ryanzhumich.github.io/

With a global population of over 1 billion people aged 60 and above, there is a rapidly increasing need for innovative age tech solutions to improve the quality of life of older adults. Conversational assistants, powered by cutting-edge technologies in Artificial Intelligence (AI), Natural Language Processing (NLP), and Large Language Models (LLM), are permeating into home care, assisted living, and nursing facilities for smart elderly care. One type of conversational assistant is task- oriented, which can significantly enhance the life experience for senior people by helping them with real-world complex daily tasks. A task-oriented virtual assistant facilitates daily tasks spanning diverse scenarios such as calling for help in response to emergencies, helping with online grocery shopping, recommending cooking recipes, managing smart home devices, and providing financial education and decision-making. It greatly promotes the life quality of older adults by improving their well-being, efficiency, safety, and independence. In this proposal, we design, develop, and deploy a task-oriented multimodal conversational assistant to help older adults with daily tasks. The innovation of this proposal lies in the fact that we will employ a human-centered participatory approach by emphasizing collaboration between designers and end-users through interviewing, prototyping, and testing to address their unique needs and preferences to improve their daily lives.

Awardee Organization(s): New York University Rory Meyers College of Nursing
Principal Investigator(s): Ab Brody, PhD, RN, FAAN
Official Project Title: Aliviado Dementia Care Machine Learning Algorithm Development for Caregiving
AITC Partner: PennAITech
Website(s):
https://www.aliviado.org
http://nursing.nyu.edu
https://www.hign.org

Care partners (CP) of persons living with dementia (PLWD) provide crucial support and find significant meaning in the care they provide. They show compassion to those they are caring for, and resilience in the face of adversity. Yet, many CP lack high-quality, evidence-based guidance for addressing care needs of PLWD. One key area that is often challenging to CP, yet where they have little support, is in addressing neuropsychiatric symptoms (NPS) such as agitation or wandering. Most PLWD experience more than one NPS at a time and thus not only do CP lack support in managing these symptoms, they don’t know which symptom to focus on first to reduce their burden and improve the quality of life of the PLWD. This is particularly true in underserved and marginalized communities who are less likely to have access to comprehensive dementia care or supportive services. Higher NPS, particularly in marginalized CP, greatly increases the risk of CP burden, physical and mental health challenges. To help CP make decisions about what NPS to prioritize, we will use artificial intelligence/machine learning (AI/ML) to develop a precision clinical decision support algorithm to assist CP in prioritizing which NPS to treat. The algorithm will be inserted into a user-friendly smartphone application which CP can download through the iOS or Android app store. The app will increase access to high-quality dementia support, empower CP to better manage NPS and improve the quality of life for both themselves and the PLWD.