Basic Information

Awardee Organization(s): University of Pennsylvania
Principal Investigator(s): Gary Weissman, MD, MSHP
Official Project Title: Advancing Diagnostic Excellence for Older Adults Through Collective Intelligence and Imitation Learning
AITC Partner: PennAITech
Website(s): https://www.med.upenn.edu

Diagnostic errors are common in the primary care setting and lead to direct patient harms, increased healthcare costs, and decrease patient satisfaction. Older adults are especially at risk for such diagnostic errors because of their higher comorbidity burden, medical complexity, increased rates of frailty and cognitive impairment, and decreased representation in clinical datasets and research studies. Artificial intelligence (AI) and machine learning (ML) methods have good face validity for offering clinical decision support (CDS) in this setting to promote diagnostic excellence. However, there is little data to suggest that any particular diagnostic CDS is transparent, reproducible, equitable, and effective at improving the diagnostic process. Therefore, our objective is to create a diagnostic CDS system for use in primary care clinics to facilitate the diagnostic process, present suggestions about important features of the history and exam to consider that are tailored to patient characteristics, and promote diagnostic excellence for older adults. This proposal overcomes existing limitations to training diagnostic CDS systems in primary care where there is a broad diagnostic scope and tremendous clinical uncertainty around training labels. To accomplish this, we will rely on imitation learning and collective intelligence to build AI/ML models that provide predicted suggestions into diagnosis and tests based on expected behaviors from peer clinicians caring for similar patients. These models will be trained using existing data from the electronic health record and deployed in a pilot study across diverse primary care clinics to assess their diagnostic accuracy and acceptability to clinicians, patients, and their caregivers.

Awardee Organization(s): University of Virginia | University of Pennsylvania
Principal Investigator(s): Aidong Zhang, PhD | Carol Manning, PhD | Li Shen, PhD | Mary Regina Boland, PhD, MPhil
Official Project Title: Fairness and Robust Interpretability of Prediction Approaches for Aging and Alzheimer’s Disease
AITC Partner: PennAITech
Website(s):
https://engineering.virginia.edu
https://www.cs.virginia.edu/~az9eg/website/home.html
https://www.med.upenn.edu

Machine learning (ML) approaches have been increasingly used for facilitating clinical decision-making in Alzheimer’s Disease (AD) and AD related dementia (ADRD). However, recent research has shown that existing ML techniques are prone to unintentional biases towards protected attributes such as age, race, sex, gender, and/or ethnicity. Moreover, although deep learning (DL) models have been a great success in many applications including AD/ADRD prediction, DL models are usually expressed in a way that is not interpretable. Thus, ML approaches using health data may incur ethical and trustworthiness concerns that may result in the unfair treatment of patients. As decision-making systems for aging and AD/ADRD become popular, a major challenge is how to ethically integrate AI/ML methods into the lives of people, given that ethical principles may often be violated in existing methods. This has become an important issue for both the ML community and the AD/ADRD community. Moreover, ML approaches that are not transparent can be prone to repeating discriminatory patterns from prior data or generating new ones based on biased learned patterns. This project develops electronic health records (EHRs) based ML methods for Penn Medicine EHR AD/ADRD datasets that are fair, generalizable, and interpretable solutions that would help inform the clinician for AD/ADRD diagnosis and care management. We focus on studying fairness and interpretability, two important factors for making AI methods trustworthy, particularly during deployment or use of the methods. We study how bias affects our prediction models. Also, we will develop explainable methods to increase clinical interpretability.

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.