Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Enhancing Heart Disease Prediction With Reinforcement Learning And Data Augmentation, Gayathri R., Sangeetha S. K. B., Sandeep Kumar Mathivanan, Hariharan Rajadurai, Benjula Anbu Malar Mb, Saurav Mallik, Hong Qin

Computer Science Faculty Publications

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning's skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation …

Enhanced Skin Cancer Diagnosis Through Grid Search Algorithm-Optimized Deep Learning Models For Skin Lesion Analysis, Rudresh Pillai, Neha Sharma, Sheifali Gupta, Deepali Gupta, Sapna Juneja, Saurav Malik, Hong Qin, Mohammed S. Alqahtani, Amel Ksibi

Computer Science Faculty Publications

Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)-CNN1, CNN2, and CNN3-each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal …

Evaluation Of Cold Atmospheric Plasma For The Decontamination Of Flexible Endoscopes, R. C. Hervé, Michael G. Kong, Sudhir Bhatt, Hai-Lan Chen, E. E. Comoy, J-P. Deslys, T. J. Secker, C. W. Keevil

Bioelectrics Publications

Background: Despite adherence to standard protocols, residues including live microorganisms may remain on the various surfaces of reprocessed flexible endoscopes. Prions are infectious proteins notoriously difficult to eliminate.

Aim: We tested the potential of cold atmospheric plasma (CAP) for the decontamination of flexible endoscope various surfaces, measuring total proteins and prion-residual infectivity as an indicator of efficacy.

Methods: New PTFE endoscope channels and metal test surfaces spiked with test soil or prion-infected tissues were treated using different CAP-generating prototypes. Surfaces were then examined for the presence of residues using very sensitive fluorescence epi-microscopy. Prion residual infectivity was determined using the …

Social Determinants Of Mental Health Considerations For Counseling Children And Adolescents, Kaprea F. Johnson, Patrick D. Cunningham, Camila Tirado, Oswaldo Moreno, Nancy N. Gillespie, Bisola Duyile, Dequindre C. Hughes, Emily Goodman Scott, Dana Brookover

Counseling & Human Services Faculty Publications

Childhood is a crucial period for developing a strong social and emotional foundation critical for mental well-being. Given that the childhood developmental period is vital for mental health, it is essential to address social determinants that can disrupt development and have lasting adverse effects on psychological and physical health. Therefore, professional counselors specializing in children and adolescent mental health care can benefit from understanding and utilizing the social determinants of mental health (SDOMH) framework in practice. This article details the SDOMH framework and considerations for SDOMH-informed treatments for professional counselors working in settings with children and adolescents.

An Acute Respiratory Distress Syndrome Drug Development Collaboration Stimulated By The Virginia Drug Discovery Consortium, John S. Lazo, Ruben M.L. Colunga-Biancatelli, Pavel A. Solopov, John D. Catravas

Bioelectrics Publications

The genesis of most older medicinal agents has generally been empirical. During the past one and a half centuries, at least in the Western countries, discovering and developing drugs has been primarily the domain of pharmaceutical companies largely built upon concepts emerging from organic chemistry. Public sector funding for the discovery of new therapeutics has more recently stimulated local, national, and international groups to band together and focus on new human disease targets and novel treatment approaches. This Perspective describes one contemporary example of a newly formed collaboration that was simulated by a regional drug discovery consortium. University of Virginia, …

Heart Disease Prediction Using Stacking Model With Balancing Techniques And Dimensionality Reduction, Ayesha Noor, Nadeem Javaid, Nabil Alrajeh, Babar Mansoor, Ali Khaqan, Safdar Hussain Bouk

School of Cybersecurity Faculty Publications

Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the …

Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)

Electrical & Computer Engineering Faculty Publications

According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical …

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …

Prediction Of Rapid Early Progression And Survival Risk With Pre-Radiation Mri In Who Grade 4 Glioma Patients, Walia Farzana, Mustafa M. Basree, Norou Diawara, Zeina Shboul, Sagel Dubey, Marie M. Lockheart, Mohamed Hamza, Joshua D. Palmer, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Rapid early progression (REP) has been defined as increased nodular enhancement at the border of the resection cavity, the appearance of new lesions outside the resection cavity, or increased enhancement of the residual disease after surgery and before radiation. Patients with REP have worse survival compared to patients without REP (non-REP). Therefore, a reliable method for differentiating REP from non-REP is hypothesized to assist in personlized treatment planning. A potential approach is to use the radiomics and fractal texture features extracted from brain tumors to characterize morphological and physiological properties. We propose a random sampling-based ensemble classification model. The proposed …

Virtual Surgical Planning In Craniomaxillofacial Surgery: A Structured Review, Kaye Verlarde, Rentor Cafino, Armando Isla Jr., Karen Mae Ty, Xavier-Lewis Palmer, Lucas Potter, Larry Nadorra, Luchin Valrian Pueblos, Lemuel Clark Velasco

Electrical & Computer Engineering Faculty Publications

Craniomaxillofacial (CMF) surgery is a challenging and very demanding field that involves the treatment of congenital and acquired conditions of the face and head. Due to the complexity of the head and facial region, various tools and techniques were developed and utilized to aid surgical procedures and optimize results. Virtual Surgical Planning (VSP) has revolutionized the way craniomaxillofacial surgeries are planned and executed. It uses 3D imaging computer software to visualize and simulate a surgical procedure. Numerous studies were published on the usage of VSP in craniomaxillofacial surgery. However, the researchers found inconsistency in the previous literature which prompted the …

A Quality Improvement Project To Improve Management Of Urinary Tract Infections In A System Of Pediatric Urgent Care Centers, Benjamin Klick, Tammy Speerhas, Jessica Parrott, Jeffrey Bobrowitz, Anne Mcevoy, Debra Conrad, Theresa Guins

Ellmer School of Nursing Faculty Publications

Background and objective: Urinary tract infections (UTIs) are a common problem in pediatric urgent care medicine. There are multiple quality improvement (QI) projects related to the management of UTIs documented in the pediatric literature. We developed a project to decrease the prescribing of ultimately unneeded antibiotics for possible UTIs in a pediatric urgent care setting. A similar project has not been described in the pediatric literature.

Methods: We first reviewed the charts of patients presenting to a system of pediatric urgent care centers with a possible UTI over a 2-year period. We then launched a QI project with three plan, …

A Comparison Of Chief Complaints, Specific Diagnoses, And Demographics Of Pediatric Urgent Care Visits Before And During The Covid-19 Pandemic: A Retrospective Study, Zaharoula A. Viennas, Julie Martin, Benjamin Klick, Tammy Speerhas, Turaj Vazifedan, Danielle Millspaugh, Jennifer Ferris, Margret Bedle, Lauren Paluch, Theresa Guins

Ellmer School of Nursing Faculty Publications

There was an increased incidence of pediatric patients who presented with injuries caused by falls not related to sports or other recreational activities, as well as for animal bites, during the early pandemic period of April 2020. Education of parents and caregivers of young children is warranted to raise awareness of the even greater potential for falls and animal bites when children are confined at home for longer than typical periods of time, as occurred with the stay-at-home government orders during the initial period of the COVID-19 pandemic.

Identifying The Serious Clinical Outcomes Of Adverse Reactions To Drugs By A Multi-Task Deep Learning Framework, Haochen Zhao, Peng Ni, Qichang Zhao, Xiao Liang, Di Ai, Shannon Erhardt, Jun Wang, Yaohang Li, Jiianxin Wang

Computer Science Faculty Publications

Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the …

Msdrp: A Deep Learning Model Based On Multisource Data For Predicting Drug Response, Haochen Zhao, Xiaoyu Zhang, Qichang Zhao, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell …

Foundations Of Plasmas For Medical Applications, T. Von Woedtke, Mounir Laroussi, M. Gherardi

Electrical & Computer Engineering Faculty Publications

Plasma medicine refers to the application of nonequilibrium plasmas at approximately body temperature, for therapeutic purposes. Nonequilibrium plasmas are weakly ionized gases which contain charged and neutral species and electric fields, and emit radiation, particularly in the visible and ultraviolet range. Medically-relevant cold atmospheric pressure plasma (CAP) sources and devices are usually dielectric barrier discharges and nonequilibrium atmospheric pressure plasma jets. Plasma diagnostic methods and modelling approaches are used to characterize the densities and fluxes of active plasma species and their interaction with surrounding matter. In addition to the direct application of plasma onto living tissue, the treatment of liquids …

A Clinical Study Of Venereal And Non Venereal Genital Dermatoses In Women, Lakkireddygari Sujana, Savitha L. Beergouder, Alekhya Rallapalli, Prasanthi Chidipudi, Sujatha Alla

Engineering Management & Systems Engineering Faculty Publications

Background: Any genital lesion or related symptoms are erroneously considered to be sexually transmitted as it is the most covered regions of the body and seems truly to be a forgotten pelvic organ it is the significant and important group of dermatological conditions may be associated with considerable morbidity, discomfort, and embarrassment. The most common conditions seen in a Dermatology Clinic are vulvar dermatoses, which comprise of lichen sclerosis, lichen planus, vulvar eczema, and psoriasis. Other conditions such as vulvar pain syndromes, vulvar disorders associated with systemic diseases, and blistering diseases are also seen.

Materials and Methods: This was a …

Activation Of Cannabinoid-2 Receptor Protects Against Pseudomonas Aeruginosa Induced Acute Lung Injury And Inflammation, Nagaraja Nagre, Gregory Nicholson, Xiaofei Cong, Janette Lockett, Andrew C. Pearson, Vincent Chan, Woong-Ki Kim, K. Yaragudri Vinod, John D. Catravas

Bioelectrics Publications

Background

Bacterial pneumonia is a major risk factor for acute lung injury (ALI) and acute respiratory distress syndrome (ARDS). Pseudomonas aeruginosa (PA), an opportunistic pathogen with an increasing resistance acquired against multiple drugs, is one of the main causative agents of ALI and ARDS in diverse clinical settings. Given the anti-inflammatory role of the cannabinoid-2 receptor (CB2R), the effect of CB2R activation in the regulation of PA-induced ALI and inflammation was tested in a mouse model as an alternative to conventional antibiotic therapy.

Methods

In order to activate CB2R, a selective synthetic agonist, JWH133, was administered intraperitoneally (i.p.) to C57BL/6J …

Why Do Family Members Reject Ai In Health Care? Competing Effects Of Emotions, Eun Hee Park, Karl Werder, Lan Cao, Balasubramaniam Ramesh

Information Technology & Decision Sciences Faculty Publications

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find …

Neuromotor Changes In Participants With A Concussion History Can Be Detected With A Custom Smartphone App, Christopher K. Rhea, Masahiro Yamada, Nikita A. Kuznetsov, Jason T. Jakiela, Chanel T. Lojacono, Scott E. Ross, F. J. Haran, Jason M. Bailie, W. Geoffrey Wright

Rehabilitation Sciences Faculty Publications

Neuromotor dysfunction after a concussion is common, but balance tests used to assess neuromotor dysfunction are typically subjective. Current objective balance tests are either cost- or space-prohibitive, or utilize a static balance protocol, which may mask neuromotor dysfunction due to the simplicity of the task. To address this gap, our team developed an Android-based smartphone app (portable and cost-effective) that uses the sensors in the device (objective) to record movement profiles during a stepping-in-place task (dynamic movement). The purpose of this study was to examine the extent to which our custom smartphone app and protocol could discriminate neuromotor behavior between …

Robust Testing Of Paired Outcomes Incorporating Covariate Effects In Clustered Data With Informative Cluster Size, Sandipan Dutta

Mathematics & Statistics Faculty Publications

Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a cluster size) is correlated to the paired outcomes or the paired differences. There have been some attempts to develop robust rank-based tests for comparing paired outcomes in such complex clustered data. Most of these existing rank tests developed for paired outcomes in clustered data compare the marginal distributions in a pair and ignore any covariate effect on …

Robust Meta-Analysis For Large-Scale Genomic Experiments Based On An Empirical Approach, Sinjini Sikdar

Mathematics & Statistics Faculty Publications

BACKGROUND: Recent high-throughput technologies have opened avenues for simultaneous analyses of thousands of genes. With the availability of a multitude of public databases, one can easily access multiple genomic study results where each study comprises of significance testing results of thousands of genes. Researchers currently tend to combine this genomic information from these multiple studies in the form of a meta-analysis. As the number of genes involved is very large, the classical meta-analysis approaches need to be updated to acknowledge this large-scale aspect of the data.

METHODS: In this article, we discuss how application of standard theoretical null distributional assumptions …

Refinement Of Alphafold2 Models Against Experimental And Hybrid Cryo-Em Density Maps, Maytha Alshammari, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Recent breakthroughs in deep learning-based protein structure prediction show that it is possible to obtain highly accurate models for a wide range of difficult protein targets for which only the amino acid sequence is known. The availability of accurately predicted models from sequences can potentially revolutionise many modelling approaches in structural biology, including the interpretation of cryo-EM density maps. Although atomic structures can be readily solved from cryo-EM maps of better than 4 Å resolution, it is still challenging to determine accurate models from lower-resolution density maps. Here, we report on the benefits of models predicted by AlphaFold2 (the best-performing …

There's An App For That: Promoting Health App Use In Rural Ireland, Noor Yahya, Marcus Simon

Undergraduate Research Symposium

Problem Statement: Smartphones and mobile applications (commonly referred to as apps) were first introduced in the late 20th century and early 21st century. Due to the public’s time constraints, lack of transportation, lack of medical insurance, and a growing desire for healthier lifestyles, the total global mHealth market forecast to reach 100 billion dollars in 2021 – a fivefold increase from 21 billion in 2016. mHealth apps have been successfully used for health promotion activities but barriers such as lack of knowledge and comfort in using health apps exist.

Purpose: Evaluate readiness of a rural community and the …

Attitudes Of Virginia Dentists Toward Dental Therapists: A Pilot Study, Adaira L. Howell, Susan Lynn Tolle, Emily A. Ludwig, Denise M. Claiborne

Dental Hygiene Faculty Publications

Purpose: The purpose of this pilot study was to determine perceptions of Virginia (VA) dentists toward mid-level dental providers, specifically dental therapists (DT), and determine whether membership in the American Dental Association (ADA) membership affected attitudes.

Methods: A convenience sample of 1208 dentists in the state of VA were invited to participate in an electronic survey. The instrument consisted of 11 Likert type scale questions assessing attitudes toward DTs. Additional items included the appropriate level of education and supervision of a DT, and five demographic questions. Descriptive statistics were used to analyze the data. A one-sample t-test was used to …

The Influence Of Sonographer Experience On Skeletal Muscle Image Acquisition And Analysis, Joshua C. Carr, Gena R. Gerstner, Caleb C. Voskuil, Joel E. Harden, Dustin Dunnick, Kristin M. Badillo, Jason I. Pagan, Kylie K. Harmon, Ryan M. Girts, Jonathan P. Beausejour, Matt S. Stock

Human Movement Studies & Special Education Faculty Publications

The amount of experience with ultrasonography may influence measurement outcomes while images are acquired or analyzed. The purpose of this study was to identify the interrater reliability of ultrasound image acquisition and image analysis between experienced and novice sonographers and image analysts, respectively. Following a brief hands-on training session (2 h), the experienced and novice sonographers and analysts independently performed image acquisition and analyses on the biceps brachii, vastus lateralis, and medial gastrocnemius in a sample of healthy participants (n = 17). Test–retest reliability statistics were computed for muscle thickness (transverse and sagittal planes), muscle cross-sectional area, echo intensity and …

A Deblurring/Denoising Corrected Scintigraphic Planar Image Reconstruction Model For Targeted Alpha Theory, Charles Ross Schmidtlein, Matthew Maroun, Andrzej Krol, Howard Gifford, Lisa Bodei, Joseph O'Donoghue, Ida Häggström, Yuesheng Xu, Si Li

Mathematics & Statistics Faculty Publications

Scintigraphy is a common nuclear medicine method to image molecular target’s bio-distribution and pharmacokinetics through the use of radiotracers and gamma cameras. The patient’s images are obtained by using a pair of opposing large flat gamma ray detectors equipped with parallel-hole lead or tungsten collimators that preferentially detect gamma-rays that are emitted perpendicular to the plane of the detector. The resulting images form an anterior/posterior (A/P) planar image pairs. The obtained images are contaminated by noise and contain artifacts caused by gamma-ray attenuation, collimator penetration, scatter and other detrimental factors. Post-filtering of the images can reduce the noise, but at …

A Crisis Of Erasure: Transgender And Gender-Nonconforming Populations Navigating Breast Cancer Health Information, Curtis Shane Tenney, Karl J. Surkan, Lynette Hammond Gerido, Dawn Betts-Green

STEMPS Faculty Publications

In this paper, we use the topic of breast cancer as an example of health crisis erasure in both informational and institutional contexts, particularly within the transgender and gender-nonconforming population. Breast cancer health information conforms and defaults to conventional cultural associations with femininity, as is the case with pregnancy and other “single-sex” conditions (Surkan, 2015). Many health information and research practices normalize sexualities, pathologize non-normative gender (Drescher et al., 2012; Fish, 2008; Müller, 2018), and fail to recognize gender-nonconforming categories (Frohard‐Dourlent et al., 2017). Because breast cancer health information is sexually normalized, an information boundary exists for the LGBTQ+ community, …

Simultaneous Wound Border Segmentation And Tissue Classification Using A Conditional Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Manisa Pipattanasomporn, Ozgur Guler

Engineering Technology Faculty Publications

Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images …

The Enlightening Role Of Explainable Artificial Intelligence In Chronic Wound Classification, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Umit Cali, Ozgur Guler

Engineering Technology Faculty Publications

Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies …

Disordered Eating, Food Insecurity, And Weight Status Among Transgender And Gender Nonbinary Youth And Young Adults: A Cross-Sectional Study Using A Nutrition Screening Protocol, Whitney R. Linsenmeyer, Ian M. Katz, Jaime L. Reed, Andrea M. Giedinghagen, Christopher B. Lewis, Sarah K. Garwood

Psychology Faculty Publications

Purpose: The purpose of this study was to describe the prevalence of and relationships among disordered eating, food insecurity, and weight status among transgender and gender nonbinary youth and young adults.

Methods: This cross-sectional study involved a screening protocol to assess disordered eating and food insecurity risk from September to December of 2019 at a gender clinic using five validated measures: (1) previous eating disorder diagnosis (yes/no); (2) Sick, Control, One Stone, Fat, Food Questionnaire (SCOFF); (3) Adolescent Binge Eating Disorder Questionnaire (ADO-BED); (4) Nine-Item Avoidant/Restrictive Food Intake Disorder Screen (NIAS); and (5) Hunger Vital Sign. Age, assigned sex at …