Biomedical Commons^™

Effects Of Environmental Stressors On Human Tissue-On-A-Chip Platforms, Andie Padilla

Open Access Theses & Dissertations

As space exploration begins to extend beyond low earth orbit, it has become increasingly critical to understand the interaction of the extreme environment of space flight with human systems. While it is known that space-travel induces a vast array of complications to cardiac, neural, musculoskeletal, and immune systems, the mechanisms by which these complications occur are poorly understood. Current research to study the effects of microgravity and radiation are limited to ground simulations, which rarely account for the multifactorial stressors experienced during spaceflight, or long duration studies aboard the International Space Station. Similarly, traditional two-dimensional (2D) models lack the ability …

A Deep Learning Approach For Semantic Segmentation And Its Application On Ctc., Samir Farag Harb

Electronic Theses and Dissertations

This dissertation explores the modeling and analysis of medical images, focusing on the intricate task of colon segmentation and subsequent 3D reconstruction, which are critical steps in Computed Tomography Colonography (CTC) systems. The primary objective of this research is to develop precise segmentation approaches to enhance the accuracy of colon identification and reconstruction from abdominal CT scans. Three distinct segmentation approaches are proposed and evaluated: a Markov Random Field (MRF)-based approach, a convolutional neural network (CNN)-based deep learning (DL) approach, and a sequential episodic training with dual contrastive learning Approach (G-SET-DCL) that has a flavor of few-shot learning (FSL). To …

Ac Electric Fields Manipulate And Concentrate Dna Molecules On Electrodes, Akila Wijesinghe, Dharmakeerthi Nawarathna

Graduate Student Government Association Research Conference

Point-of-care (POC) electric field-based biosensors have emerged as a promising tool to detect early cancer biomarkers such as circulating tumor DNA (ctDNA), microRNA (miRNA), and proteins. To be effective in screening in clinical settings, these biosensors must be simple and easy to use. In this study, we have studied the manipulation of short DNA molecules suspended in a sessile drop to achieve this goal. Alternative current (AC) electric fields were used to polarize DNA molecules and produce dielectrophoretic (DEP) force on DNA molecules. DEP force is used to manipulate polarized DNA molecules towards the higher electric field gradients (toward the …

A Parallel Fuzzy Logic Framework For Surgical Skill Evaluation Via Instance Segmentation And Deepsort Tracking, Mohsen M. Mohaidat

Dissertations

Manual evaluation of suturing skills during laparoscopic training is often subjective and labor-intensive, resulting in the lack of scalable and consistent feedback for trainees. This study proposes an automated framework that not only significantly reduces the need for in-person assessment by experts but also ensures scalability, thereby addressing the objectivity and cost-effectiveness limitations. While low-cost laparoscopic box trainers have become increasingly popular for residency training, performance assessment still depends on expert supervision. The proposed system aims to alleviate these limitations.

This study introduces a novel automated framework incorporating an optimized DeepSORT algorithm for classifying, localizing, and tracking surgical tools using …

The Interplay Of Accommodation And Oculomotor Vergence Within Young Adults With Binocularly Normal Vision And Typically-Occurring Convergence Insufficiency, Sebastian Fine

Dissertations

Concerted binocular coordination evoking oculomotor and refractive responses to visual stimuli are essential to daily function. Oculomotor dysfunctions can inhibit binocular responses to visually-near stimuli and have high comorbidities to accommodative dysfunctions. Three visual cues for inward (convergent) and outward (divergent) oculomotor movements, when presented concertedly create natural-viewing conditions: disparity- the binocular difference in light cast onto the fovea due to differing ocular perspectives, blur- the acuity of a visual target which stimulates accommodation, and proximal- the perceived distance of a visual stimuli based on size.

This study aims to quantitatively investigate oculomotor vergence and accommodation performances between individuals with …

Real-World Implementation Of A Noninvasive, Ai-Augmented, Anemia-Screening Smartphone App And Personalization For Hemoglobin Level Self-Monitoring, Robert G. Mannino, Julie Sullivan, Jennifer K. Frediani, Paul George, Jeremy Whitson, James Tumlin, L. Andrew Lyon, Erika A. Tyburski, Wilbur A. Lam

Engineering Faculty Articles and Research

Anemia, characterized by low blood hemoglobin (Hgb) levels, afflicts >2 billion individuals worldwide. Here, we report real-world data generated by a smartphone app that noninvasively screens for anemia using only “fingernail selfies.” App data for anemia screening were obtained from >1.4 million uses across the United States enabling geographic mapping of Hgb levels. Of those, 9,061 users also self-reported complete blood count Hgb levels for comparison, resulting in accuracy and performance that match gold standard laboratory testing and a sensitivity and specificity of 89% and 93%, respectively, when using an anemia cutoff of 12.5 g/dL. Geotagged data enabled construction of …

Fibrin-Polycaprolactone Scaffolds For The Differentiation Of Human Neural Progenitor Cells Into Dopaminergic Neurons, Salma Paulina Ramirez

Open Access Theses & Dissertations

This project aimed to develop a tissue-on-a-chip platform for studying Parkinson's Disease (PD) using dopaminergic (DA) neurons. PD is a neurodegenerative disorder characterized by progressive loss of DA neurons, leading to involuntary movements and other symptoms. Early diagnosis and deeper understanding of PD pathogenesis are crucial for improving disease management and patient outcomes. To model PD in vitro, this research utilized human-induced pluripotent stem cell (hiPSC)-derived neural progenitor cells (NPCs) cultured on electrospun (ES) polycaprolactone (PCL) scaffolds. Given PCL's hydrophobicity, ECM-based biomaterial coatings, including Cell Basement Membrane (CBM) proteins, Matrigel, and Fibrin, were explored to enhance NPC adhesion, differentiation, and …

An Automatic Colorectal Polyps Detection Approach For Ct Colonography., Mohamed Yousuf

Electronic Theses and Dissertations

Colon cancer, also known as colorectal cancer, is a significant health concern, with increasing incidence rates, particularly among individuals under 50. This rise has led experts to recommend the introduction of regular screenings at 45 years of age for adults at average risk. Early detection through such screenings can identify precancerous polyps, allowing their removal before they develop into cancer. This proactive approach has the potential to reduce colorectal cancer deaths by up to 60%. In addition, research indicates that people diagnosed before age 50 have better survival rates, which emphasizes the importance of early diagnosis. Therefore, adhering to recommended …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

Optimizing And Training An Svm-Based Breast Cancer Tumor Classifier, Kevin Lopatka

Master's Theses

With advancements in technology, turning to machine learning has become a popular choice for aiding clinicians in the diagnoses of breast cancer malignancies. While the neural networking approach has been vetted thoroughly, this work aims to take advantage of traditional machine learning techniques; mainly support vector machine learning and the optimizing of feature extraction. The discrete-wavelet transform is used in the feature extraction stage of machine learning. Previous works that use this feature extraction technique are analyzed and expanded upon by utilizing a variety of different wavelets as well as other color-spaces with the goal of achieving higher result metrics …

Deep Learning-Based Multi-Class Classification Of Breast Cancer Ultrasound Images Using Convolutional Neural Networks, Andres E. Dewendt Urdaneta

ATU Scholars Symposium

The National Cancer Institute forecasts 2,001,140 cancer diagnoses in 2024, with approximately 600,000 expected deaths. Breast cancer is projected to be the most prevalent, with about 310,000 cases. Early diagnosis is critical to improving outcomes, and various diagnostic technologies, including imaging, biopsies, and blood tests, play a vital role. Image testing methods include X-rays, ultrasounds, magnetic resonance imaging (MRI), and PET scans. Artificial intelligence (AI) has recently significantly improved cancer detection, improving speed, accuracy, and effectiveness. This research project uses a convolution neural network (CNN) to analyze ultrasound breast images, classifying them as benign, malignant, or normal. Our CNN model …

Removing Eog Artifacts From Eeg Recordings Using Deep Learning, Christian O'Reilly, Scott Huberty

Faculty Publications

The electroencephalogram (EEG) directly measures the electrical activity generated by the brain. Unfortunately, it is often contaminated by various artifacts, notably those caused by eye movements and blinks (EOG artifacts). Such artifacts are usually removed using an independent component analysis (ICA) or other blind source separation techniques. However, it is difficult to assess whether subtracting EOG components estimated through ICA removes some neurogenic activity. It is crucial to address this question to avoid biasing EEG analyses. Toward that objective, we developed a deep learning model for EOG artifact removal that exploits information about eye movements available through eye-tracking (ET). Using …

A Reliable And Efficient Detection Pipeline For Rodent Ultrasonic Vocalizations, Sabah Shahnoor Anis, Devin Mark Kellis, Kris Ford Kaigler, Marlene A. Wilson, Christian O'Reilly

Faculty Publications

Analyzing ultrasonic vocalizations (USVs) is crucial for understanding rodents' affective states and social behaviors, but the manual analysis is time-consuming and prone to errors. Automated USV detection systems have been developed to address these challenges. Yet, these systems often rely on machine learning and fail to generalize effectively to new datasets. To tackle these shortcomings, we introduce ContourUSV, an efficient automated system for detecting USVs from audio recordings. Our pipeline includes spectrogram generation, cleaning, pre-processing, contour detection, post-processing, and evaluation against manual annotations. To ensure robustness and reliability, we compared ContourUSV with three state-of-the-art systems using an existing open-access USV …

Clinical Use Of Sit2stand Ai Application For Kinematic Analysis In Prosthesis Users., Samerial Brown

Posters - 2025

Biomechanical analysis is a tool to evaluate prosthetic and orthotic patient's. These tools offer the clinician capability of understanding the mechanism of injury, gait deviation or prosthesis problem. Video based analysis require expensive hardware, software, and training which sometimes costs $40-100,000.

The recent advent of artificial intelligence (AI) has opened up the possibility of acquiring high speed human motion video analysis using low-cost hardware and open-source machine learning algorithms. Still, free assessments like the Sit2Stand test is a current clinical outcome measure which assesses ability of a patient to stand and sit as fast as possible 5x. The faster the …

Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review, Lauren E. Perkins

Honors College Theses

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified …

Deep Learning Applications For Predictive Modeling In Cancer Therapy And Enzyme Encoding, Mengmeng Liu

LSU Doctoral Dissertations

Predictive modeling has revolutionized computational biology and molecular bioinformatics, enabling significant advancements in cancer therapy and functional enzyme characterization. Despite considerable progress, significant challenges remain in accurately predicting combinational cancer therapies and systematically representing enzyme functions for computational applications. Traditional methods struggle with capturing the complex interactions between drugs and biological networks, as well as representing hierarchical relationships within enzyme classifications. This dissertation addresses these limitations by developing advanced deep learning models tailored to enhance predictive performance in both domains.

First, a data augmentation strategy is introduced to improve anticancer drug synergy prediction by generating pharmacologically relevant drug pairs based …

Electrochemical Detection Of Dopamine Using Screen-Printed Graphene Electrode For Cancer Diagnosis And Therapy, Pritu P. Sarkar, Nazmul Islam

Research Symposium

Background: Dopamine plays a critical role in various essential functions, including motor control, hormone regulation, cognition, learning, and the reward system. In healthy individuals, dopamine levels are extremely low, with concentrations ranging from 0 to 0.25 nM in blood and 0.3 to 3.13 µM in urine. Abnormal levels are linked to disorders like Parkinson’s, schizophrenia, Alzheimer’s, epilepsy, hypertension, and arrhythmia. Abnormal dopamine levels can indicate the presence of certain cancers. Dopamine receptors may be therapeutic targets for treating cancer, especially breast and colon cancer. Thus dopamine can increase the efficacy of anticancer drugs in breast and colon cancer. Detecting dopamine …

Conversational Voice User Interfaces Supporting Individuals With Down Syndrome: A Literature Review, Franceli L. Cibrian, Concepción Valdez, Lauren Min, Vivian Genaro Motti

Engineering Faculty Articles and Research

Conversational Voice User Interfaces (CVUIs) are widely used in commercial applications such as personal assistants. CVUIs are beneficial for most users as they enable interaction through speech and natural language. However, recent studies indicate that underrepresented user groups, such as individuals with speech impairments and specifically those with Down syndrome, face challenges in using voice commands to control CVUIs. The anatomical and physiological differences affecting the voice, speech, fluency, and prosody of users with Down syndrome hinder their experience with CVUIs. This article presents the results of 43 papers related to the use of voice user interfaces supporting individuals with …

Limitations In Speech Recognition For Young Adults With Down Syndrome, Franceli L. Cibrian, Yingying 'Yuki' Chen, Kayla Anderson, Cecilia Marie Abrahamsson, Vivian Genaro Motti

Engineering Faculty Articles and Research

Speech recognition has the potential to make technology more accessible to users. However, the accuracy of speech recognition remains limited for users with disabilities, including those with Down Syndrome, and the types and frequencies of recognition errors are poorly understood. This paper characterizes these problems, focusing on errors occurring when recognizing Down Syndrome speech. We analyze the transcripts from six speech recognition algorithms (Google, IBM, Otter.ai, Microsoft, AssemblyAI, OpenAI) using the audio content of 15 individuals with Down Syndrome (331 dialogues; 3428 words). Our analysis shows: (1) significant difference in speech recognition accuracy for people with Down Syndrome compared to …

Medical Ai: Solving Healthcare Challenges And Inspiring Ai Innovation, Xiaowei Yu

Computer Science and Engineering Dissertations - Archive

Artificial Intelligence (AI) is transforming healthcare by enabling large-scale analysis of medical data and integrating multimodal information for more comprehensive diagnostics. I present my work addressing fundamental and challenging problems in developing state-of-the-art AI models for medical data analysis, including multimodal brain data and other medical datasets. Additionally, I design brain-inspired AI models by integrating insights from organizational principles of brain networks. Specifically, my research tackles three critical aspects: (1) AI in Computational Neuroscience, where I design deep learning models for brain network analysis to uncover the organizational principles of brain networks; (2) Brain-Inspired AI, where I integrate superior brain …

Neurovascular Coupling Impairments In Acute Traumatic Brain Injury: An Eeg-Nirs Analysis, Zachary Armstrong

Bioengineering Theses - Archive

Traumatic brain injury (TBI) is a major cause of neurological impairment, often leading to variable recovery and uncertain prognosis in the neurocritical care setting. There is a pressing clinical need for robust, physiologically grounded biomarkers to inform prognosis and therapeutic decision-making in acute TBI. This thesis investigates neurovascular coupling (NVC), the physiological coordination between neuronal activity and cerebral blood flow, as a candidate biomarker for brain function and recovery after injury.

A prospective cohort study was performed using simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings in patients with moderate-to-severe TBI and healthy controls. Wavelet transform coherence (WTC) analysis was …

Cochlear Electrode Insertion Training Model, Sarah Powell, Kaelyn E. Kraley, Nathan J. Smith

Williams Honors College, Honors Research Projects

Cochlear implant surgery is a delicate procedure performed by Otolaryngologists (ENTs) to implant an electronic device into the inner ear to provide a sense of sound for people who are profoundly deaf or hard of hearing. The current practices of training involve cadavers and 3D-printed models. Cadavers are commonly used but are expensive, single-use, and do not provide visual and haptic feedback, which are essential for medical students. 3D printed models are less commonly used and are hard to fabricate and not as realistic. If medical students are not properly trained for this delicate procedure, then risks are significantly increased …

A Novel Real-Time Threshold Algorithm For Closed-Loop Epilepsy Detection And Stimulation System, Liang Hung Wang, Zhen Nan Zhang, Chao Xin Xie, Hao Jiang, Tao Yang, Qi Peng Ran, Ming Hui Fan, I. Chun Kuo, Zne Jung Lee, Jian Bo Chen, Tsung Yi Chen, Shih Lun Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Epilepsy, as a common brain disease, causes great pain and stress to patients around the world. At present, the main treatment methods are drug, surgical, and electrical stimulation therapies. Electrical stimulation has recently emerged as an alternative treatment for reducing symptomatic seizures. This study proposes a novel closed-loop epilepsy detection system and stimulation control chip. A time-domain detection algorithm based on amplitude, slope, line length, and signal energy characteristics is introduced. A new threshold calculation method is proposed; that is, the threshold is updated by means of the mean and standard deviation of four consecutive eigenvalues through parameter combination. Once …

Advancing Electrical Stimulation: Full-Head Mri Segmentation For Abnormal Brain Anatomy With Tdcs, Andrew Birnbaum

Dissertations and Theses

Evaluating the effectiveness of transcranial direct current stimulation (tDCS) is essential for guiding its integration into therapeutic and performance-enhancement applications. In our laboratory, we investigate the efficacy of tDCS across multiple experimental models, including both animal and human studies. I have contributed significantly to the execution and analysis of these experiments, which include studies in rats and healthy human participants aimed at evaluating whether electrical stimulation of the motor cortex can enhance motor learning. These studies assess improvements in fine motor performance resulting from tDCS. In stroke patients, I contribute to our investigation of tDCS as a rehabilitative intervention, particularly …

Laser-Induced Graphene For Early Disease Detection: A Review, Sri Ramulu Torati, Gymama Slaughter

Center for Bioelectronics Publications

Electrochemical biosensors have been instrumental in early disease detection, facilitating effective monitoring and treatment. The emergence of graphene has significantly advanced sensor technology in various fields, including biomedicine, electronics, and energy. In this landscape, laser‐induced graphene (LIG) has emerged as a superior alternative to conventional graphene synthesis methods. Its straightforward fabrication process and compatibility with wearable devices boost its practicality and potential for real‐world applications. This review highlights the transformative potential of LIG in biosensing, showcasing its contributions to the development of next‐generation diagnostic tools for early disease detection. An overview of the LIG synthesis process and its applications in …

Identification Of Fiducial Points In Seismocardiographic Cycles Using Manual And Automated Annotation Methods, Jasmine-Vy T. Truong

Honors Undergraduate Theses

There is currently a need for complementary methods for non-invasive cardiac monitoring. Seismocardiography (SCG), the measurement of cardiac-induced vibrations at the chest surface, has shown potential clinical utility. Improving the reliability of detecting fiducial points of electrocardiography (ECG) and SCG, which collectively capture the electro-mechanical cardiac activities, could expand ECG/SCG utility as a low-cost, accessible tool for clinical assessment. This study identifies commonly accepted criteria for fiducial point detection in SCG and ECG through an extensive literature review and signal processing techniques. The previous criteria were evaluated to identify their strengths and weaknesses. Based on the findings, an improved set …

Advanced Nanocomposite-Based Electrochemical Sensor For Ultra-Sensitive Dopamine Detection In Physiological Fluids, Megha Shinde, Gymama Slaughter

Center for Bioelectronics Publications

This study presents a novel point-of-care electrochemical sensor for dopamine (DA) detection, featuring a flexible laser-induced graphene (LIG) modified with a unique nanocomposite comprising Nb₄C₃T_x MXene, polypyrrole (PPy), and iron nanoparticles (FeNPs). The LIG-Nb₄C₃T_x MXene-PPy-FeNPs is characterized by scanning electron microscopy to confirm the successful surface modification. The electrochemical performance of the fabricated sensor via cyclic voltammetry showed significant electrochemical activity upon Nb₄C₃T_x MXene-PPy-FeNPs nanocomposite modification of the LIG surface with an increased peak anodic current (Ipa) from 43 μA to 104 μA. The sensor …

Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

High grade gliomas are infiltrating tumors characterized by their diffusive invasion and proliferative growth. Across and within patients heterogeneity of tumors makes it challenging to determine tumor spatial extent after surgical resection. Traditionally, tumor growth predictions after surgical resections rely on generalized models and population-based observations, which do not account for individual patient differences. To address this gap, we propose a personalized approach with image-guided computational model (digital twin) that incorporates physics-based modeling to predict tumor recurrence. Our digital twin involves an inverse modeling step, followed by a recurrence model that accounts for varying surgical effects. The physics-guided inverse model …

Detecting Sars-Cov-2 In Ct Scans Using Vision Transformer And Graph Neural Network, Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

Electrical & Computer Engineering Faculty Publications

The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was …

A Method For Ultrasound Servo Tracking For Puncture Needle, Shitong Ye, Bo Yang, Hao Quan, Shan Liu, Minyu Tang, Jiawei Tian

Electrical & Computer Engineering Faculty Publications

Computer-aided surgical navigation technology helps and guides doctors to complete the operation smoothly, which simulates the whole surgical environment with computer technology, and then visualizes the whole operation link in three dimensions. At present, common image-guided surgical techniques such as computed tomography (CT) and X-ray imaging (X-ray) will cause radiation damage to the human body during the imaging process. To address this, we propose a novel Extended Kalman filter-based model that tracks the puncture needle-point using an ultrasound probe. To address the limitations of Kalman filtering methods based on position and velocity, our method of Kalman filtering uses the position …