Computer Sciences Commons^™

Artificial Intelligence In Radiology, Olivia Sweeney

Theses, Dissertations and Capstones

Introduction: Artificial intelligence (AI) has increasingly transformed radiologic practice by improving diagnostic accuracy, streamlining workflows, and reducing interpretation errors. As AI integration has expanded across imaging modalities, questions have emerged regarding its effectiveness compared to traditional radiologist-only interpretation.

Purpose of Study: The purpose of this study has been to evaluate the impact of AI-assisted radiology on diagnostic accuracy, efficiency, and error reduction, while also assessing clinician perceptions of AI as a collaborative tool in imaging analysis.

Methodology: This qualitative study has used a systematic review of peer-reviewed literature published between 2015 and 2025, following PRISMA guidelines, combined with an interview …

Online Learning-Driven Human Intent Estimation And Control For Human-Robot Interaction, Irfan Ganie, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a novel Stackelberg-game theoretic multilayer-online learning framework for cooperative control of nonlinear Physical Human-Robot Interaction (pHRI), where the human is modeled as the leader guiding a robot follower. This hierarchical interaction is captured as a dynamic Stackelberg game, with the human's intention estimated in real-time through online multilayer neural networks (MNNs). We introduce SVD-based weight update laws for actor-critic MNNs, which approximate value functions and control inputs for both human and robot, eliminating the need for predefined basis functions. In this framework, the human objective is first inferred and used to guide the robot actions by shaping …

Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses the infinite horizon optimal tracking control problem for partially uncertain control-affine nonlinear discrete-time (DT) systems, where the control input dynamics are known. Multi-layer critic and actor neural networks (MNNs) are utilized for online estimation of the infinite horizon value function and optimal control input. The NN weights are tuned online using a direct temporal difference error (TDE)-driven learning approach, which modifies the singular values of the gradient with respect to the NN weights to accelerate their convergence. The critic NN uses a novel experience replay technique to improve sample efficiency without introducing biased TDEs and guarantee the …

Multi-Model Safe Neuro-Optimal Output Tracking Control Of Autonomous Surface Vessels With Explainable Ai, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a safety-aware deep reinforcement learning (DRL)-based trajectory tracking control of autonomous surface vessels (ASVs). A multilayer neural network (MNN) observer estimates the ASV's state and uncertain dynamics. By utilizing the estimate state vector from the observer, a safety-aware DRL-based optimal policy is formulated using control barrier function (CBF) and Karush-Kuhn-Tucker (KKT) conditions. An actor-critic MNN with singular value decomposition (SVD)-based update mitigates vanishing gradients. To enhance adaptability, an online safe lifelong learning (SLL) scheme counters catastrophic forgetting across varying ASV dynamics. The Shapley Additive Explanations (SHAP) method identifies key features influencing the control policy. Simulations on an …

Online Adaptive Optimal Tracking Control Of Uncertain Strict Feedback Discrete-Time Systems With Hardware Verification Using A Quadrotor Uav, Maxwell Geiger, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This article considers the infinite time horizon optimal tracking control problem for discrete time (DT) partially uncertain strict feedback systems with application to quadrotor UAVs. First, the strict feedback DT system is transformed into an equivalent affine nonlinear DT system in terms of tracking error dynamics. The optimal tracking control problem is solved using an augmented system approach, where a horizon of future reference trajectory points are used in the augmented state, as compared to using a single point. The internal dynamics of the original nonlinear strict feedback system and the transformed affine system in terms of error dynamics are …

Reinforcement Learning-Based Nonlinear Optimal Discrete-Time Control Of Power Systems, Vijay Kumar Singh, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a partially model-free adaptive optimal tracking control method for power systems, specifically targeting a synchronous generator connected through a reactive transmission line. By integrating the tracking error dynamics with reference trajectory dynamics, an augmented system is created. A discounted performance function is introduced to address the nonlinear tracking problem optimally. Unlike traditional methods that compute feedforward and feedback terms separately, the proposed approach calculates both simultaneously by minimizing the discounted performance function. The discrete-time tracking Bellman and Hamilton-Jacobi-Bellman (HJB) equations are derived, and a reinforcement learning (RL)-based technique is employed to solve the optimal policy online without …

Artificial Intelligence In Cancer-Related Malnutrition And Cachexia: A Transformative Tool In Clinical Nutrition, Salvatore Carbone

EVMS School of Health Professions Faculty Publications

[Introduction] Malnutrition and cachexia are common complications in cancer patients, and they negatively influence prognosis, treatment efficacy, and tolerability as well as quality of life [[1], [2], [3]]. Accurately identifying and effectively managing malnutrition and cachexia in this population remains a clinical challenge. Conventional validated screening tools may lack the sensitivity and specificity required for early detection and personalized intervention in diverse cancer types and treatment settings [4,5]. Over the last decade, the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) strategies, has shown promising results in clinical nutrition, with the potential to revolutionize nutritional …

Enhanced Continual Reinforcement Learning-Based Output Feedback Control Of Heterogeneous Quadrotors Formation, Ehsan Soleimani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a unified framework for the safe and optimal control of heterogeneous quadrotor unmanned aerial vehicles (QUAVs) in formation, enabling multitask missions without requiring precise system dynamics. To address partial state observability, a multilayer neural network (MNN) observer is designed to estimate unmeasured states. Reinforcement learning (RL) is employed for optimal control utilizing an MNN ensuring adaptability. Barrier Lyapunov Functions (BLFs) are integrated into the RL framework to enforce safety by maintaining QUAVs within predefined constraints. An enhanced continual learning (ECL) method is proposed to improve the adaptability of MNNs. This method enables effective multitask learning while mitigating …

Optimizing Decision-Making In A Cerebral Palsy Model Using Reinforcement Learning, Richard Ampah

Pitzer Senior Theses

This study presents an original interdisciplinary investigation into how reinforcement learning (RL) can model motor and cognitive defects and potentially improve motor and cognitive functions in individuals with cerebral palsy (CP), a non-progressive neurological disorder that impairs movement and adaptability. Integrating computational neuroscience and machine learning, the research applies policy gradient methods and Markov Decision Processes (MDPs) to simulate adaptive learning in agents with and without CP-related constraints.

The central aim is to compare the cumulative rewards of optimal policies, derived from value iteration, and human-like learning policies using the REINFORCE algorithm, both with and without the Bellman baseline. The …

Revealing Spatiotemporal Neural Activation Patterns In Electrocorticography Recordings Of Human Speech Production By Mutual Information, Julio Kovacs, Dean Krusienski, Minu Maninder, Willy Wriggers

Mechanical & Aerospace Engineering Faculty Publications

Background

Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.

New Method

We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, …

An Analytical Model Of Motion Artifacts In A Measured Arterial Pulse Signal—Part Ii: Tactile Sensors, Md Mahfuzur Rahman, Subodh Toraskar, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

This paper, the second of two parts, presents an analytical model of motion artifacts (MA) in measured pulse signals by a tactile sensor, which contains a deformable microstructure sitting on a substrate. While the tissue-contact-sensor (TCS) stack and the sensor are both treated as a 1DOF (degree-of-freedom) system, tissue–sensor contact joins their mass together to form a 1DOF system with springs and dampers on both sides. MA on the sensor substrate causes baseline drift and time-varying system parameters (TVSP) of the TCS stack simultaneously. An analytical model is developed to mathematically relate baseline drift and TVSP to a measured pulse …

Applying Machine Learning Methods To Generate Understandings Of Differential Item Functioning In A Flu Knowledge Assessment, William L. Romine, Tanvi Banerjee, Derrick Cox

Computer Science and Engineering Faculty Publications

Current influenza trends, including the severity of the 2025 flu season and the prevalence of H5 bird flu in livestock, necessitate efforts to better understand how to educate students about its transmission. Although validated assessments of influenza knowledge exist, these have not been evaluated for affective and demographic biases. We explore differential item functioning (DIF) effects in four items focused on specific aspects of flu transmission derived from a validated influenza knowledge assessment. In doing so, we introduce and utilize a machine learning framework for exploration of DIF which offers greater flexibility than traditional statistical approaches in terms of studying …

Why Ai Monitoring Faces Resistance And What Healthcare Organizations Can Do About It: An Emotion-Based Perspective, Karl Werder, Lan Cao, Eun Hee Park, Balasubramaniam Ramesh

Information Technology & Decision Sciences Faculty Publications

Continuous monitoring of patients' health facilitated by artificial intelligence (AI) has enhanced the quality of health care, that is, the ability to access effective care. However, AI monitoring often encounters resistance to adoption by decision makers. Healthcare organizations frequently assume that the resistance stems from patients' rational evaluation of the technology's costs and benefits. Recent research challenges this assumption and suggests that the resistance to AI monitoring is influenced by the emotional experiences of patients and their surrogate decision makers. We develop a framework from an emotional perspective, provide important implications for healthcare organizations, and offer recommendations to help reduce …

A Vision Transformer Based Assistive System For Dermatological Diagnosis In Systemic Lupus Erythematosus, Syeda Lamima Farhat

All Graduate Theses, Dissertations, and Other Capstone Projects

Systemic Lupus Erythematosus (SLE) is a complex and often underdiagnosed autoimmune disease that affects multiple organs and presents with a wide range of symptoms-ranging from fatigue and joint pain to life-threatening organ damage. One of its most visible and diagnostically significant indicators is the Butterfly Malar Rash (BMR), a distinctive facial rash that often resembles other common dermatological conditions like rosacea, acne, eczema, and fifth disease. This overlap can lead to misdiagnosis or delayed detection, especially in busy clinical environments. To assist dermatologists in distinguishing BMR from similar facial rashes, this study explores the development of an AI-powered image classification …

Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced By Statistical Analysis, Esin Bilgin

Theses, Dissertations and Culminating Projects

Cancer is a serious and severe cause seen in every region of the world and severely affects the quality of life and life span. Among the various types of cancer, lung cancer is one of the most critical, having a fatal impact on life. While medical imaging techniques, laboratory results, and biomarkers play a significant role in diagnosis and prognosis, clinical studies are also crucial in monitoring the progression of cancer and identifying diagnostic and prognostic factors. The findings demonstrate satisfactory accuracy, and the analysis incorporates statistical data with machine learning techniques. These findings play a pivotal role in supporting …

Machine Learning Models For Pancreatic Cancer Survival Prediction: A Multi-Model Analysis Across Stages And Treatments Using The Surveillance, Epidemiology, And End Results (Seer) Database, Aditya Chakraborty, Mohan D. Pant

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Pancreatic cancer is among the most lethal malignancies, with poor prognosis and limited survival despite treatment advances. Accurate survival modeling is critical for prognostication and clinical decision-making. This study had three primary aims: (1) to determine the best-fitting survival distribution among patients diagnosed and deceased from pancreatic cancer across stages and treatment types; (2) to construct and compare predictive risk classification models; and (3) to evaluate survival probabilities using parametric, semi-parametric, non-parametric, machine learning, and deep learning methods for Stage IV patients receiving both chemotherapy and radiation. Methods: Using data from the SEER database, parametric models (Generalized Extreme Value, …

Understanding Physiological Responses For Intelligent Posture Detection Using Wearable Technology, Chaitanya Vardhini Anumula, Tanvi Banerjee, Anuradha Oak

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

This study investigates the physiological impact of Iyengar yoga at the pose-level using EmbracePlus wearable smartwatch, for data recording and personalized yoga pose detection for tracking.

Motion Artifacts (Ma) At-Rest In Measured Arterial Pulse Signals: Time-Varying Amplitude In Each Harmonic And Non-Flat Harmonic-Ma Coupled Baseline, Md Mahfuzur Rahman, Mamun Hasan, Zhili Hao

Mechanical & Aerospace Engineering Faculty Publications

Motion artifacts (MA) cause great variability in a measured arterial pulse signal, and treatment of MA solely as a baseline drift (BD) fails to eliminate its effect on the measured signal. This paper presents a study on the effect of MA at rest (< 0.7 Hz) on measured arterial pulse signals using a microfluidic-based tactile sensor. By taking full account of the dynamic behavior of the transmission path from the true pulse signal in an artery to a measured pulse signal at the sensor, the tissue-contact-sensor (TCS) stack, an analytical model of MA in a measured pulse signal is developed. In this model, the TCS stack is treated as a 1DOF system for its dynamic behavior; MA is quantified as the displacement (i.e., BD) and time-varying system parameters (TVSP) of the TCS stack. The mathematical expression of MA in a measured pulse signal reveals that while BD remains as low-frequency additive noise, TVSP causes time-varying harmonics in a measured pulse signal. Further time-frequency analysis (TFA) of measured pulse signals validates the existence of TVSP and, for the first time, reveals its effect on a measured pulse signal: time-varying amplitude in each harmonic and non-flat harmonic-MA-coupled baseline.

Enhancing Public Health Surveillance: Development And Validation Of Machine Learning Models For Suspected Opioid Overdose Detection In Emergency Medical Services Data, Peter J. Rock

Theses and Dissertations--Clinical and Translational Science

The ongoing opioid overdose crisis in the United States requires timely and accurate surveillance systems to inform public health responses. Traditional public health surveillance methods rely on hospital discharge data and death certificates, which suffer from significant reporting delays and miss cases where patients refuse hospital transportation. Emergency Medical Services (EMS) data presents a promising alternative with advantages in timeliness and case ascertainment but lacks validated definitions for suspected opioid overdose (SOO).

This dissertation addresses this critical gap through the development, validation, and fairness assessment of machine learning models with natural language processing (ML-NLP) for identifying SOOs in EMS data. …

Predicting Mental Health Disparities Using Machine Learning For African Americans In Southeastern Virginia, Ismail El Moudden, Michael C. Bittner, Matvey V. Karpov, Isaac O. Osunmakinde, Akosua Acheamponmaa, Breshell J. Nevels, Mamadou T. Mbaye, Tonya L. Fields, Karthiga Jordan, Messaoud Bahoura

Department of Obstetrics & Gynecology Faculty Publications

This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from African American adults (18–85) in Southeastern Virginia (2016–2020), we found Mood Affective Disorders were most prevalent (41.66%), followed by Schizophrenia Spectrum and Other Psychotic Disorders. Females predominantly experienced mood disorders, with patient ages typically ranging from late thirties to mid-forties. Medicare coverage was notably high among schizophrenia patients, while emergency admissions and comorbidities significantly impacted total healthcare charges. Machine learning models, including gradient boosting, random forest, neural networks, logistic regression, and Naive Bayes, were validated through 100 repeated 5-fold cross-validations. …

Training Set Augmentation And Harmonization Enables Radiomic Models To Detect Early Onset Of Lung Cancer, Claire Huchthausen, Menglin Shi, Gabriel L.A. Sousa, James Larner, Einsley Janowski, Jonathan Colen, Krishni Wijesooriya

Data Science Faculty Publications

Radiomics-based machine learning models have the potential to detect lung cancer at inception from CT scans and transform patient outcomes. Low malignancy rates in early-development pulmonary nodules (PNs) and variable image acquisition hinder development of clinically applicable radiomics-based early detection models. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We first trained machine learning models to predict PN malignancy using radiomic features from scans of early-development benign and malignant PNs (n = 187) harmonized using ComBat. Observing near-chance performance, we augmented training with later-development benign and malignant PNs (n = 225). We evaluated …

A Happy Medium?: Using Image Generators To Explore Solution-Focused Art Therapy’S Miracle Question, Daniel A. Hernried

Art Therapy | Master's Theses

This mixed methods, randomized, single-session study tested whether integrating text-to-image generations into Solution-Focused Brief Art Therapy alters therapeutic rapport and short-term outcomes relative to traditional artmaking materials. Participants were assigned by coin flip to create using either a text-to-image generator or convention media (23 per group), completing immediate and three-day follow-ups. Alliance was measured using DREAM (Dimensions of Regard, Empathy, and Authenticity Metric), and problems were rated pre/post; groups did not differ significantly on DREAM total or facets, and both modalities produced reliable pre-to-post reductions in problem severity. At the same time, process differences were pronounced: the AI condition showed …

A Spine-Specific Lexicon For The Sentiment Analysis Of Interviews With Adult Spinal Deformity Patients Correlate With Sf-36, Sf-36, And Odi Scores: A Pilot Study Of 25 Patients, Ross Gore, Michael M. Safaee, Christopher J. Lynch, Christopher P. Ames

VMASC Publications

Classic health-related quality of life (HRQOL) metrics are cumbersome, time-intensive, and subject to biases based on the patient’s native language, educational level, and cultural values. Natural language processing (NLP) converts text into quantitative metrics. Sentiment analysis enables subject matter experts to construct domain-specific lexicons that assign a value of either negative (−1) or positive (1) to certain words. The growth of telehealth provides opportunities to apply sentiment analysis to transcripts of adult spinal deformity patients’ visits to derive a novel and less biased HRQOL metric. In this study, we demonstrate the feasibility of constructing a spine-specific lexicon for sentiment analysis …

A Framework For Mixed Reality Within Healthcare Education, Benyamin Ebadinia

Theses

Understanding complex three-dimensional systems and spatial relationships is a recurring difficulty in healthcare education, where students are often expected to reason about internal structures and multi-system processes from 2D diagrams, textbook figures, and static mannequins. This thesis presents the design, implementation, and mixed-methods evaluation of Systems Simulation, a reusable mixed reality (MR) application intended to help undergraduate nursing students explore human anatomy and pathophysiology using immersive 3D visualization.

Built in C# with the StereoKit framework for Microsoft HoloLens 2, Systems Simulation organizes nine anatomical body systems within a shared application. Learners can select a system, anchor the model in their …

Using Visual Prompts To Analyze Ejection Fraction In Echocardiograms, Kevin Reisch

Graduate Theses, Dissertations, and Problem Reports (ETD)

Left ventricular ejection fraction (LVEF) is a critical biomarker for heart failure, but manual estimation from echocardiograms is time-consuming. Artificial intelligence can be used to accelerate this process, allowing clinicians to focus on other critical tasks. Current methods typically train models from scratch on echocardiogram datasets; however, this approach is limited by the scarcity of large medical imaging datasets, which are expensive and difficult to acquire. We present a transfer learning approach that leverages pretrained models from massive datasets, enabling continuous improvement as foundation models advance. Our method employs visual prompting to generate trainable masks for echocardiogram videos, transforming the …

Symbol-Temporal Consistency Self-Supervised Learning For Robust Time Series Classification, Kevin Garcia, Cassandra Garza, Brooklyn Berry, Yifeng Gao

Computer Science Faculty Publications

The surge in the significance of time series in digital health domains necessitates advanced methodologies for extracting meaningful patterns and representations. Self-supervised contrastive learning has emerged as a promising approach for learning directly from raw data. However, time series data in digital health is known to be highly noisy, inherently involves concept drifting, and poses a challenge for training a generalizable deep learning model. In this paper, we specifically focus on data distribution shift caused by different human behaviors and propose a self-supervised learning framework that is aware of the bag-of-symbol representation. The bag-of-symbol representation is known for its insensitivity …

A Retrieval Augmented Approach To Improving Accuracy Of Biomedical Term Normalization By Large Language Models, Thanh Son Do

Graduate Theses/Dissertations

Ontology normalization is crucial in biomedical text processing, as it enables the mapping of medical expressions to standardized ontology terms and their corresponding identifiers. This thesis explores the feasibility of using large language models (LLMs) for ontology normalization, with a specific focus on the Human Phenotype Ontology and Gene Ontology. Prior research studies indicated that LLMs employing zero-shot learning tend to exhibit low accuracy and are prone to frequent hallucinations. We propose a retrieval augmented generation (RAG) approach to address these limitations and enhance normalization accuracy. We generated synthetic test sets of ontology-derived synonyms to evaluate normalization performance and developed …

Optimization Of Serum And Salivary Cortisol Interpolation For Time-Dependent Modeling Frameworks In Healthy Adult Males, Nathaniel T. Berry, Travis Anderson, Christopher K. Rhea, Laurie Wideman

Rehabilitation Sciences Faculty Publications

Cortisol is an important marker of hypothalamic-pituitary-adrenal function and follows robust circadian and diurnal rhythms. However, biomarker sampling protocols can be labor-intensive and cost-prohibitive. Objectives: Explore analytical approaches that can handle differing biological sampling frequencies to maximize these data in more detailed and time-dependent analyses. Methods: Healthy adult males [N = 8; 26.1 (±3.1) years; 176.4 (±8.6) cm; 73.1 (±12.0) kg)] completed two 24 h admissions: one at rest and one including a high-intensity exercise session on the cycle ergometer. Serum and salivary cortisol were sampled every 60 and 120 min, respectively. Six alternative sampling profiles were defined by downsampling …

Clinician Perspectives On Virtual Reality Use In Physical Therapy Practice In The United States, Danielle T. Felsberg, Jared T. Mcguirt, Scott E. Ross, Louisa D. Raisbeck, Charlend K. Howard, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

The primary goal of physical rehabilitation is to assess movement impairments and restore function to improve overall quality of life. Virtual reality (VR) may provide the optimal environment to promote these goals due to its motivating and modifiable nature which can be difficult to accomplish through traditional real-world therapeutic methods. Current research of VR for rehabilitation has demonstrated that VR interventions can produce clinically meaningful change in motor outcomes. Despite this, adoption and usage of VR by physical therapy professionals is unclear due to the limited research in this area. Thus, the purpose of this study was to identify the …

Development Of An Ecg-Based Deep Learning Model For Pediatric Congenital Heart Disease (Chd) Diagnosis, Annbar Mekouar

Selected Full-Text Master Theses 2021-

Congenital heart disease (CHD) stands as the leading congenital anomaly which affects pediatric populations throughout the world. The effectiveness of treatment depends on both early diagnosis and accurate identification but echocardiography requires manual interpretation which proves time-consuming and inconsistent especially when examining pediatric patients with their distinct cardiac systems. The research aims to create a deep learning-based diagnostic framework which uses ECG data to identify coronary artery disease subtypes in pediatric patients. The model uses high-quality datasets from Dr. Ignacio Lugones to extract R-R intervals and QRS durations through convolutional neural networks (CNNs). The system addresses pediatric-specific challenges while enhancing …