Determining quality in forensic injury imaging - ��AV secures NIH AIM-AHEAD funding to advance equity in AI-driven injury detection

dhawkin — Fri, 22 Nov 2024 18:21:21 +0000

Determining quality in forensic injury imaging - ��AV secures NIH AIM-AHEAD funding to advance equity in AI-driven injury detection dhawkin Fri, 11/22/2024 - 13:21

In This Story

Janusz Wojtusiak, PhD

Katherine Scafide, PhD, RN, FAAN

David Lattanzi

��AV has been awarded funding from the National Institutes of Health (NIH) through its Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program.

This initiative encourages more people from historically underrepresented groups in researching and developing of artificial intelligence (AI) and machine learning (ML) models. AIM-AHEAD aims to leverage the growing volume of data generated through electronic health records (EHR) and other biomedical research to address health disparities and inequities.

Janusz Wojtusiak. Photo by the Office of University Branding

The newly funded project, led by Janusz Wojtusiak, builds upon the Equitable and Accessible Software for Injury Detection (EAS-ID) initiative aimed at building artificial intelligence tools for collecting, assessing and analyzing injury data. The new funding specifically addresses the problem of measuring equity and quality of imaging documentation.

In addition to Wojtusiak, an interdisciplinary research team comprising Katherine Scafide and David Lattanzi is joined by Health Informatics Assistant Professor Eman Elashkar, Research Assistant Professor Jesse Kirkpatrick, who is also the acting director of the Institute for Philosophy and Public Policy, and Amin Nayebi Nodoushan, a postdoctoral researcher at Mason.

Their research focuses on using AI methods combined with Alternate Light Sources (ALS) to improve bruise detection, addressing visibility issues for individuals with darker skin tones who often encounter challenges in accurately assessing injuries sustained from violence.

Current literature highlights that skin color significantly influences the accuracy of AI-based tools in healthcare. Studies have documented disparities in the performance of medical devices, such as pulse oximeters and smartwatches, which frequently yield inaccurate readings for individuals with darker skin. These discrepancies can lead to delays in critical medical interventions, exacerbating existing health disparities.

The proposed research will focus specifically on bruises, the most common type of soft tissue injury experienced by victims of intimate partner violence (IPV). Statistics indicate that approximately one in three people in the U.S. have experienced IPV, with racial minorities reporting disproportionately higher rates. Survivors with darker skin tones have noted that their bruises are often invisible, resulting in significant delays in seeking necessary medical care.

Katherine Scafide is part of the multidisciplinary team working to advance equity in AI-driven injury detection. Photo by Emma Anderson.

Leveraging the innovative use of ALS, the George Mason research team has demonstrated marked improvement in bruise visibility across diverse skin tones. The team aims to develop methods that ensure that AI-based tools provide equitable and unbiased detection and characterization of injuries. This will involve creating combined technical-ethical metrics to assess the performance of these tools across different skin tones. Engaging diverse stakeholders, including clinicians, forensic nurses, and community representatives, will be essential in the development process to align with ethical practices in AI.

The research team’s two primary aims are to develop metrics that assess equity in AI tools and apply these metrics to improve bruise detection models. They have already collected a substantial dataset of bruise images taken under various lighting conditions, which will be utilized to enhance the AI models’ performance. The interdisciplinary nature of the research team, comprising informaticians, engineers, clinicians, and ethicists, ensures a comprehensive approach to tackling these complex issues.

In alignment with AIM-AHEAD’s goals, this research initiative promises to contribute significantly to addressing health inequities and enhancing the capabilities of AI in healthcare. By focusing on the specific needs of underrepresented communities, the George Mason research team is paving the way for more equitable health care solutions, ultimately aiming to improve the accuracy and efficacy of injury assessments across diverse populations.

This project is led by George Mason’s College of Public Health in collaboration with the College of Engineering and Computing. More information on the project can be found at bruise.gmu.edu.

Topics

Bruise Visibility

Artifical Intelligence

Machine Learning

Machine Learning in Health Care

Research

New research utilizes machine learning to address social isolation among alzheimer’s caregivers

mthomp7 — Thu, 10 Oct 2024 17:22:37 +0000

New research utilizes machine learning to address social isolation among alzheimer’s caregivers mthomp7 Thu, 10/10/2024 - 13:22

A new study from the College of Public Health at ��AV, led by Professor Janusz Wojtusiak and Health Services Research doctoral candidate Ghaida Alsadah, explores the use of machine learning to predict social isolation among caregivers of individuals with Alzheimer’s disease and related disorders.

This research, which has been selected for funding by the Commonwealth of Virginia's Alzheimer's and Related Diseases Research Award Fund (ARDRAF), marks a significant advance in leveraging artificial intelligence (AI) to address a critical public health issue.

Professor Janusz Wojtusiak uses machine learning to predict social isolation among caregivers of individuals with Alzheimer’s disease and related disorders.

Alsadah has played a pivotal role in this study, contributing her expertise as a doctoral candidate to develop and refine machine learning models. These models analyze data from the Health and Retirement Study (HRS), National Health and Aging Trends Study (NHATS), and Behavioral Risk Factor Surveillance System (BRFSS) to identify and predict social isolation trajectories among caregivers. The study’s key finding is that AI-driven methods offer a novel approach to detecting social isolation, potentially leading to targeted interventions.

“This research is groundbreaking in its application of machine learning to predict social isolation among caregivers, an area previously underexplored,” said Wojtusiak. “The potential to develop AI-based interventions could significantly enhance the well-being of caregivers who often face profound social and emotional challenges.”

The study aims to construct predictive models for social isolation, adapt them for Medicare claims data, and simulate their application across large populations. The goal is to create a framework for AI-based interventions to address loneliness among caregivers effectively.

The innovation of this work lies in its use of machine learning to analyze and predict social isolation—a new approach with the potential to transform current understanding and interventions. Wojtusiak and Alsadah’s research is set to significantly impact health informatics and caregiver support. “Predicting Social Isolation Among Alzheimer’s Caregivers Using Machine Learning” will be published in an upcoming issue of a leading health informatics journal, emphasizing the importance of innovative approaches to complex public health challenges.

Additional contributors include College of Public Health alumna Mary Louise Pomeroy, a postdoctoral research fellow at Johns Hopkins University, who provided valuable expertise on social isolation and relevant datasets.