PennAITech Awardees

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): University of Pittsburgh
Principal Investigator(s): Julie Faieta, PhD, MOT OTR/L
Official Project Title: Health App Review Tool: Connecting those Affected by Alzheimer’s to Needed Technology Support
AITC Partner: PennAITech
Website(s): https://www.shrs.pitt.edu/

The goal of this project is to connect those affected by Alzheimer’s disease and related dementias (ADRD) with effective apps using an intelligent decision-making aid, the Health App Review Tool (HART). The HART is comprised of a User Assessment and App Assessment, that together characterize the features of health apps relative to the needs, abilities, and preferences of individuals with ADRD and their informal caregivers. The HART assesses the goodness of match between user and app variables in order to guide app selection.
The first phase of this pilot project will be used to develop a web-base and app interface to house the HART. The dedicated interface is necessary in preparation for real-world and wide-spread use of the HART. There will be a user interface displaying the HART assessment questions, a back end that completes the scoring process, and a results display. In addition, we will establish a cloud-based library of app scores that can be downloaded and compared to new HART users in the future. The second phase of the project will be a usability study to gather feedback and insight on the HART interfaces for those impacted by ADRD.
The Health App Review Tool (HART) is expected facilitate clinicians, caregivers, and community organizations to select the best apps to meet the unique needs of individuals with ADRD and their caregivers. Improving access to person centered, easy to use technology guidance is intended to increase the impact and equity of app-mediated care.