Machine Learning in Health Care

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

Symptoms and Movement Patterns During the COVID-19 Pandemic

dhawkin — Mon, 22 Feb 2021 21:55:42 +0000

Symptoms and Movement Patterns During the COVID-19 Pandemic dhawkin Mon, 02/22/2021 - 16:55

In This Story

Janusz Wojtusiak, PhD

How can we better understand how people move during the pandemic and how they spread COVID-19? Dr. Janusz Wojtusiak, associate professor of health informatics and director of the Machine Learning Inference Lab is leading one of the first individual-level studies on social distancing.

Participants use the Mason COVID Health Check^TM to record symptoms of possible COVID-19 infection and GPS and WiFi data to provide information on how they move during the pandemic. This allows the researchers to model and predict movements during the pandemic and in conjunction with any reported possible COVID-19 symptoms. This could help inform effective public health interventions to reduce infection.

Initial findings from the first wave of the study, including data collected through September 2020, were published in the Journal of Healthcare Informatics Research. Wojtusiak and colleagues found that headache was the most frequently reported symptom and headache was always listed as a symptom when any other symptoms were reported. The next most commonly reported symptoms were cough and sore throat.

Movement patterns varied among participants, with some only going out for essential trips while others moved about more. As a group, movement was consistent over the study period, which included a period when Virginia was under a stay-at-home order and when it was not. Participants traveled a total average of 139 miles per week, visiting an average of less than six locations per week. This low average mileage and number of sites visited does suggest that COVID-19-related restrictions affected their movement. However, they also found that even when participants reported symptoms of COVID-19 or contact with others with COVID-19, they did not change their movements as recommended by public health guidance.

This research is possible thanks to the dedication of study participants who share their data to allow for movement modeling. Recruitment of Mason faculty, staff, and students for the second wave of the study has begun. Learn more and sign up: https://www.mli.gmu.edu/sd/.

��AV has a very low COVID-19 infection rate, and during the period none of the study participants reported COVID-19 infection, so researchers weren’t able to link COVID-19 positive tests and movement. Future analysis will include data from the 2020 winter so may provide more information on movement after COVID-19 infection. The researchers will also conduct surveys and interviews to provide richer data including reasons for complying or not complying with social distancing.

In a related study supported by the National Cancer Institute, Wojtusiak’s team analyzed individual movements of people on campus. Such micro-scale movements within buildings can be modeled using WiFi data collected each time a mobile phone or laptop is connected to the internet. In over 150 simulated scenarios they were able to reconstruct movements of volunteers within Mason’s Peterson Hall. This technology is intended to support contact elicitation as part of contact tracing for COVID-19 or other infectious diseases.

Promising preliminary results show that the technology can change how public health officials think about contact tracing. Tests are now being conducted across other locations on Fairfax campus. You can learn more about the project at https://www.mli.gmu.edu/wifi.

Topics

CHHS Research

Machine Learning

Machine Learning in Health Care

coronavirus; covid-19

College of Health and Human Services

College of Health and Human Services Department of Health Administration and Policy

CHHS News

Panagiota Kitsantas, PhD

admin_alpha — Tue, 20 Oct 2015 23:24:01 +0000

Panagiota Kitsantas, PhD admin_alpha Tue, 10/20/2015 - 19:24

Professor, HAP

Contact Information

Email: pkitsant@gmu.edu

Phone: 703-993-9164
Building: Peterson Hall
Room 4412

CV

Download Panagiota Kitsantas curriculum vitae (CV) here.

Biography

Dr. Yiota Kitsantas, Professor of Biostatistics and Epidemiology in the Department of Health Administration and Policy, has extensive research experience in the fields of child and maternal health, especially around utilizing data mining techniques to address various public health issues in these populations. In her research, the use of these methodologies and large exiting data sets have addressed research gaps pertaining to risk profiles of childhood obesity, prenatal alcohol use, pregnancy likelihood in young adults and several other research topics. She collaborates with a large number of researchers from various areas and serves as a biostatistics consultant on numerous projects. In one of the most recent projects, Dr. Kitsantas served as a biostatistician and co-Investigator on a funded project by the National Institutes of Health (R15 grant) to examine rapid weight gain among infants of low income Hispanic immigrant mothers. Dr. Kitsantas serves on editorial boards and as a reviewer for various peer-reviewed journals related to maternal and child health. She has published manuscripts in peer reviewed journals such as the Journal of Asthma, Journal of Perinatal Medicine, Archives of Gynecology and Obstetrics, Childhood Obesity, Public Health, Women’s Health Issues, Journal of Maternal-Fetal & Neonatal Medicine, Early Human Development, among others. Dr. Kitsantas frequently presents research findings at national and international conferences.

Research

Research Interests

Applying data mining techniques and methods of categorical data analysis to investigate public health related issues in the fields of maternal and infant health and substance abuse among adolescents

Select Publications

Kitsantas, P., & Aguisanda, F. (2016). Risk of Asthma among Adolescents with Obesity Exposed to Environmental Tobacco Smoke. Journal of Asthma, 53(1), 25-29.

Kitsantas, P., Gallo, S., Palla, H., Nguyen, V., & Gaffney, K.F. (2016). Nature and Nurture in the Development of Childhood Obesity: Early Infant Feeding Practices of Overweight/Obese Mothers Differ Compared to Mothers of Normal Body Mass Index. Journal of Maternal-Fetal & Neonatal Medicine, 29(2), 290-293.

Kornides, M., Kitsantas, P., Lindley, L., & Wu, C. (2015). Factors associated with young adults' pregnancy likelihood. Journal of Midwifery & Women's Health, 60(2), 158-168.

Kitsantas, P., Gaffney, K.F., & Wu, H. (2015). Identifying high-risk subgroups for alcohol consumption among younger and older pregnant women. Journal of Perinatal Medicine, 43(1), 43-52.

Kitsantas, P., Gaffney, K.F., Castello, J., & Wu, H. (2014). Determinants of alcohol cessation, reduction and no reduction during pregnancy. Archives of Gynecology and Obstetrics, 289(4), 771-779.

Kornides, M., & Kitsantas, P. (2013). Evaluation of Breastfeeding Promotion, Support, and Knowledge of Benefits on Breastfeeding Outcomes. Journal of Child Health Care, 17(3), 264-273.

Kitsantas, P., Kornides, M., Cantiello, J., & Wu, H. (2013). Chronic physical health conditions among Children of Different Racial/Ethnic Backgrounds. Public Health, 127(6),546-553.

Grants

"Evidence NOW: Advancing Heart Health in Primary Care, Heart of Virginia Healthcare," Agency for Healthcare Research and Quality (AHRQ), subcontract with Virginia Commonwealth University. Goldberg, D. (Principal Investigator), 2017-2018. Role: Biostatistician. $414,755.

"Rapid Weight Gain among Infants of Low Income Hispanic Immigrant Mothers," National Institutes of Health (R15). Gaffney, K. (Principal Investigator), 2014-2018. Role: Co-Investigator. $445,704.

"Prenatal Alcohol Consumption and Maternal and Infant Health," National Institute on Alcohol Abuse and Alcoholism (R03). 2017 (in progress). Role: Principal Investigator (Gaffney, K., Co-Investigator). $50,000 (pending).

"Artificial Intelligence in Data Analysis," U.S. Department of Veterans Affairs, Office of Research & Development. Wojtusiak, J. (Principal Investigator), 2013-2014. Role: Co-Investigator. $200,000.

"Quality of life indicators among residents in assisted living communities," The Assisted Living Federation of America (ALFA). Gimm, G. (Principal Investigator), 2012-2014. Role: Co-Investigator. $97,000.

"Predictors of Excessive Weight Gain among Infants of Low Income Hispanic Mothers: A Comparison with the National Infant Feeding Practices Study-II," Mason-Inova Fund. Gaffney, K. (Principal Investigator), 2012-2014. Role: Co-Investigator. $49,000.

"Determinants of Alcohol Reduction and Cessation during Pregnancy," Foundation for Alcohol Research. 2012-2013. Role: Principal Investigator (Gaffney, K., Co-Investigator). $37,000.

"Child Health Outcomes, Vaccination Patterns and Familial Genetic Risk," National Vaccination Information Center. Debold, V. (Principal Investigator), 2011-2013. Role: Co-Investigator. $70,000.

"Comparison of Health and Utilization Outcomes among Fully and Never Vaccinated Children," National Vaccination Information Center. Debold, V. (Principal Investigator), 2010-2013. Role: Biostatistician. $50,000.

"Novel risk factors and management strategies for transient ischemic attack (TIA) and stroke: The INOVA Fairfax Stroke Database," GMU Office of the Vice President, Research & Economic Development. 2010-2011. Role: Principal Investigator (Benson, R., Co-Investigator). $10,000.

"The 2008 ECLS-B Training Seminar: The role of Maternal pregravid BMI in overweight and obesity among preschoolers," U.S. Department of Education's National Center for Education Statistics. 2007-2008. Role: Principal Investigator. $1,000.

"Availability and access to substance abuse programs in correctional settings," National Institute of Drug Abuse, Development opportunity/contract (Department of Criminal Justice, GMU). Taxman, F. (Principal Investigator), 2007-2008. Role: Biostatistician. $40,000.

"Fetal growth and onset of delivery in infants with birth defects," ��AV, Summer Research Funding for Tenure-Track Faculty, Office of the Vice President Research and Economic Development. 2007-2008. Role: Principal Investigator. $5,600.

"Assessing the role of intrauterine growth retardation in preterm births among infants with birth defects," East Carolina University, College Research Award. 2005-2006. Role: Principal Investigator. Sabbatical.

Honors and Awards

Teacher of Distinction Award, 2014, ��AV
Career Connection Faculty Award, 2014, ��AV

Affiliations

Member

2012-Present, Member of the APHA Maternal & Child Health Section Committee & Abstract Reviewer

American Public Health Association
Member

American Statistical Association
Member

2012-Present, Member of the Career Mentoring Committee

American College of Epidemiology
Member

Abstract reviewer

Maternal & Child Health Epidemiology, CDC

Degrees

PhD, Statistics, Florida State University
MS, Statistics, Florida State University
MS, Health Sciences, James Madison University

Machine Learning in Health Care

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

In This Story

Topics

Symptoms and Movement Patterns During the COVID-19 Pandemic

In This Story

Topics

Panagiota Kitsantas, PhD

Contact Information

CV

Biography

Research

Research Interests

Select Publications

Grants

Honors and Awards

Affiliations

Degrees

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