AITC Consortium

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): University of Pennsylvania
Principal Investigator(s): Emily Moin, MD, MBE | Scott Halpern, MD, PhD
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

Abstract: Through 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): 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.

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): University of California San Diego
Principal Investigator(s): Xinyu Zhang, PhD | Alison Moore, MD, MPH
Official Project Title: Non-Intrusive, Fine-Grained In-Home Daily Activity Transcription for Alzheimer’s Monitoring
AITC Partner: PennAITech
Website(s): http://xyzhang.ucsd.edu

Recent research identified a strong correlation between onset of Alzheimer’s disease (AD) and changes in fine physical activities, e.g., movement and dwelling time across locations, daily routines like medicine/water intake. Early detection of such indicators is crucial in compiling better treatment and slowing the progression. Conventional methods for monitoring the activities of daily living (ADL) rely on observation or self-report, which are time consuming, error-prone, and require strict patient compliance. This project aims to transcend such limitations and bridge the key technology gaps in bringing ADL sensing close to clinical practice. The project focuses on the development of EgoADL, a system that uses non-intrusive smartphone/smartwatch sensors to sense ADL. EgoADL builds on a novel self- supervised sensor fusion model that trains itself without user intervention. Instead of classifying among a small known set of ADLs, it directly transcribes raw multi-modal sensor signals into text logs of ADLs which can be interpreted by clinical practitioners or AI models. EgoADL will be the first to use non-visual sensors to transcribe fine ADLs (e.g., human-object interaction) with near-vision precision, in real-time and in a privacy-aware manner. The sensing data can facilitate follow-on clinical and AI analytics, potentially enabling early detection of chronic diseases and safe aging in place. Ubiquitous health monitoring is particularly important for rural and underserved communities, who either do not have access to or cannot afford prolonged hospitalization. EgoADL will be verified through a pilot study in UCSD’s and Upenn’s healthy aging facilities.

Awardee Organization(s): Kennesaw State University
Principal Investigator(s): Maria Valero, PhD, MsC | Katherine Ingram, PhD
Official Project Title: GlucoCheck: A Non-Invasive & AI-Assisted Blood Glucose Monitoring Device for Older Adults
AITC Partner: PennAITech
Website(s):
https://ccse.kennesaw.edu
Iotas.kennesaw.edu

The GlucoCheck project presents a significant advancement in the realm of diabetes management, with a specific focus on enhancing the quality of life for older adults. In light of the escalating global prevalence of diabetes and its associated complications, the imperative for non-invasive, accurate, and user-friendly blood glucose monitoring has never been more pronounced. Diabetes poses severe health risks, particularly for older adults, making effective and comfortable glucose monitoring paramount. Existing methods, characterized by frequent finger-pricking and subcutaneous needle implants, entail discomfort, infection risks, and potential tissue damage, particularly in individuals with diminished skin elasticity and compromised immune responses. GlucoCheck emerges as a pioneering solution, harnessing near-infrared spectroscopy (NIR) technology augmented by AI. This device offers a non-invasive means of consistently monitoring blood glucose levels by simply wearing it on one’s finger. Importantly, GlucoCheck integrates AI algorithms that adapt to individual skin attributes, including color and texture, enhancing precision across diverse demographics. This project’s core objectives encompass rigorous validation of GlucoCheck’s efficacy, with a primary emphasis on older adult demographic. Comparative analyses will be conducted, aligning GlucoCheck’s measurements with conventional blood glucose monitors to ascertain accuracy and reliability. Our mission is underscored by the desire to deliver an efficacious, user-centric device tailored to the unique requirements of older adults. The project’s outcomes will furnish valuable insights that drive refinements in GlucoCheck, propelling us closer to positively impacting the livesof millions of individuals grappling with diabetes.