AITC Consortium

Awardee Organization(s): Foresite Healthcare
Principal Investigator(s): Nicholas Kalaitzandonakes, PhD
Official Project Title: AI/ML Analyses of Mobility Changes Among Elderly Using Continuous Gait Data
AITC Partner: PennAITech
Website(s): https://www.foresitehealthcare.com/

Novel pharmacological and non-pharmacological interventions for Alzheimer’s Disease (AD) and Alzheimer’s Disease and Related Dementias (ADRD) (e.g., physical therapy, occupational therapy, exercise, etc.) can slow the disease progression, but timely diagnosis is necessary for such interventions to be effective. Yet, early diagnosis of the disease remains difficult. Various biomarkers and specialized brain scans are accurate and effective in diagnosing the disease early, but they are expensive, invasive, and difficult to execute in practice.
In previous studies, gait (e.g., walking speed) and motion characteristics (e.g., cadence, stride time and variability, step length, step width, sacrum mediolateral range of motion) have been found to strongly associate with the onset of AD/ADRD and to, often, precede cognitive decline and the presence of other dementia symptoms. As such, it may be possible to use gait and mobility features as diagnostics for AD/ADRD.
In this project, we will identify and develop gait- and motion-related predictive biomarkers for AD/ADRD. For this purpose, we will analyze multiyear gait and motion data from more than 5,000 older adults in assisted living (AL) and memory care (MC) communities around the US. Residents in MC units are all professionally diagnosed with AD/ADRD.
The identified biomarkers will be used as digital diagnostics for early, easy, and inexpensive identification of AD/ADRD, including through passive monitoring of populations in communities with care management and those aging in place (e.g., via passive, physiological, sensors and wearables).

Awardee Organization(s): University of Pennsylvania
Principal Investigator(s): Kyra O’Brien, MD, MSHP
Official Project Title: WATCH (warning assessment and alerting tool for cognitive health)
AITC Partner: PennAITech
Website(s): https://www.med.upenn.edu/kbjohnsonlab/

Early detection of cognitive impairment, including dementia, is beneficial for patients and their families because it helps them access needed care supports. Screenings for many health conditions are done in primary care, but it is difficult to do screenings for cognitive impairment in this setting. This is because the time required to do a cognitive screen can be challenging to fit into a typical primary care appointment. The goal of this project is to make it easier for primary care providers to detect early signs of cognitive impairment in real-time. We aim to develop a prediction model that uses data from the electronic health record (EHR) and video and audio from the primary care visit to estimate a patient’s risk of having undetected cognitive impairment. We will build off an existing cognitive impairment prediction model called the EHR Risk of Alzheimer’s and Dementia Assessment Rule (eRADAR). We will use this model to recruit Penn Medicine primary care patients to participate in a recorded research visit, where we will conduct a cognitive and physical exam. Video and audio data from these visits will be combined with the EHR data to generate new predictive models. We aim to see if the addition of video and audio data to the prediction model improves detection of conditions such as Alzheimer’s disease and related dementias. This will build the basis for future work aiming to integrate the new predictive model into primary care clinics and test its effectiveness in promoting early detection of cognitive impairment.

Awardee Organization(s): University of Washington
Principal Investigator(s): Mehmet Kurt, PhD
Official Project Title: An explainable deep learning framework for brain age prediction in AD
AITC Partner: PennAITech
Website(s): https://www.me.washington.edu/facultyfinder/mehmet-kurt

The biology of aging is a complex biological process that has yet to be fully understood. Recently, due to the growth in data availability and advances in deep learning techniques, brain age has been demonstrated as an effective biomarker for studying the brain aging process in the presence and absence of neurological disorders. This “brain age” provides a global estimate of how the subject’s brain deviates from the average brain of a similar age. In the PennAITech project, we will extend brain age predictions to brain anatomy by providing age for different brain regions. We will also improve the transparency and accountability of this tool by explaining brain age in terms of clinically relevant image features, e.g., explaining brain age by highlighting brain regions that indicate accelerated aging. By identifying individual patterns of brain aging and specific areas of accelerated aging, clinicians using this tool can tailor individualized interventions and prognostic strategies. This subject-specific approach can improve outcomes by addressing specific risk factors and vulnerabilities.

Awardee Organization(s): Boston University
Principal Investigator(s): Vijaya Kolachalama, PhD
Official Project Title: AI-based tool for mixed dementias
AITC Partner: PennAITech
Website(s): https://vkola-lab.github.io

Diagnosing dementia can be challenging because many different types often occur together, making it difficult to identify what is truly causing a person’s symptoms. For example, someone with both Alzheimer’s disease and vascular dementia may not respond well to treatments designed just for Alzheimer’s. Misdiagnosis can lead to ineffective or even harmful treatments, especially in older adults with complex medical histories. That’s why it’s so important to have tools that can accurately identify all the underlying causes of dementia. Our research team has developed an artificial intelligence (AI) model that can analyze a wide range of information, from brain scans and cognitive tests to medical history, to help determine what type or types of dementia a person may have. We tested this model on data from over 50,000 people and showed that it can reliably distinguish between different dementia types, even when multiple conditions are present. In some cases, neurologists using our AI tool improved their diagnostic accuracy significantly. Our next step is to build and test this AI tool in real-world healthcare settings. With support from the a2 Pilot Award, we will partner with two hospitals to test the tool on a diverse group of patients. The goal is to create a reliable, user-friendly platform that helps doctors make better decisions about diagnosing and treating dementia, ultimately improving patient care.

Awardee Organization(s): Weill Cornell Medicine
Principal Investigator(s): Nili Solomonov, PhD | Logan Grosenick, PhD
Official Project Title: Scalable subtyping for personalized assessment of late-life social disconnection
AITC Partner: PennAITech
Website(s): https://www.solomonovlab.com

Social disconnection is a growing public health crisis in the US, with half of adults reporting social isolation. It predicts accelerated brain aging, poor adherence to medical care, and decline in cognition and daily functioning. Still, there are no gold- standard, evidence-based methods to assess social disconnection in healthy older adults, highlighting the need for new approaches.
Here, we propose SOCIAL-Q (“Scalable Online Classification and Individual Assessment for Loneliness Quantification”): a scalable tool for quantification and classification of an individual’s social-emotional profile and their risk of social disconnection. This approach will provide a scalable, rapid, and precise assessment of individuals’ social-emotional functioning. It will also guide development of scalable interventions to increase social connectedness and improve well-being in healthy older adults.
To achieve this goal, we will leverage exciting developments in machine learning and computer vision including “large language models” (LLMs) for speech tracking and emotion detection from vocal prosody. We will combine these advances with multimodal subtyping methods we developed to design an automated AI-powered tool that will estimate an individual’s socio-emotional profile based on a brief multimodal assessment.
Findings from SOCIAL-Q will inform scalable, personalized, interventions aimed at increasing social connectedness that can be delivered in community settings to healthy adults (e.g., senior centers).

Awardee Organization(s): University of Pennsylvania
Principal Investigator(s): Rory Boyle, PhD
Official Project Title: Understanding aging and ADRD disparities using a representative epigenetic clock
AITC Partner: PennAITech
Website(s): https://www.pennftdcenter.org/penn-ftdc-team

Disadvantaged groups may experience accelerated biological aging, due to the cumulative impact of repeated experiences with socioeconomic adversity and marginalization. This may cause early physical and cognitive health deterioration leading to disparities in aging and Alzheimer’s disease and related dementias (ADRD) outcomes, as evident in associations of epigenetic clocks with worse aging and ADRD outcomes.
Epigenetic clocks apply machine learning to DNA methylation profiles from blood samples to estimate biological aging. Black American adults show accelerated epigenetic aging compared to White American adults and socioeconomic factors contribute to this racial disparity. However, epigenetic clocks have been developed using non-representative datasets consisting predominantly of White adults and therefore may not provide accurate estimates of epigenetic aging in other racial and ethnic groups.
We will use machine learning to develop and validate a representative epigenetic clock (REpiClock) to more accurately predict epigenetic age in Black adults. As the accuracy of epigenetic age estimates may be influenced by the genetic distance of a target individual from the average genotype of the training dataset, we will apply a data-driven method to correct epigenetic age predictions for the individual’s genetic distance from the training set. In a deeply-phenotyped biobank, we will assess the relationship of the REpiClock with ADRD pathology, using plasma biomarkers, and ADRD risk and aging outcomes, using electronic health record data. This will allow us to establish whether a representative, genetic distance-corrected epigenetic clock, more precisely estimates disparities in epigenetic aging and whether these disparities underlie disparities in ADRD pathology and risk.

Awardee Organization(s): University of Texas Health Science Center at Houston
Principal Investigator(s): Min Ji Kwak, MD, MS, DrPH | Sunyang Fu, PhD, MHI
Official Project Title: Detection of adverse drug event using NLP among older adults with heart failure
AITC Partner: PennAITech
Website(s): https://med.uth.edu/internalmedicine/2022/11/17/min-ji-kwak-md-ms-drph/

Adverse drug events (ADEs) in older adults with heart failure are a serious public health concern. These drug-related complications can be life-threatening and significantly reduce quality of life. To capture ADEs correctly, doctors need to carefully review a patient’s symptoms, medical history, prescription changes, and past records. However, this can be difficult to do thoroughly during a busy clinic visit.
An automatic tool using an Artificial Intelligence tool can help by scanning a patient’s past medical records for signs of an ADE. This tool can be built into electronic health records to provide real-time assessments. While current AI models typically handle only one task at a time—like identifying medications—ADE detection requires a more advanced system that can process multiple tasks and make complex decisions.
To address this, we are developing a specialized AI framework called AIDE4HF. This system will leverage existing tools to detect ADEs in older adults taking heart failure medications. Our project has two main goals:
1.Create a high-quality dataset of ADEs related to heart failure medications in older adults.
2.Develop and test a powerful AI system that can accurately detect these ADEs.
This research is a collaboration between UTHealth McGovern Medical School and UTHealth McWilliams School of Biomedical Informatics. By combining expertise from multiple fields, we aim to create a model that closely mimics real clinical decision-making and has the potential to be widely used in medical practice.