Investigative Techniques Commons^™

Accessible Real-Time Eye-Gaze Tracking For Neurocognitive Health Assessments, A Multimodal Web-Based Approach, Daniel C. Tisdale

Master's Theses

We introduce a novel integration of real-time, predictive eye-gaze tracking models into a multimodal dialogue system tailored for remote health assessments. This system is designed to be highly accessible requiring only a conventional webcam for video input along with minimal cursor interaction and utilizes engaging gaze-based tasks that can be performed directly in a web browser. We have crafted dynamic subsystems that capture high-quality data efficiently and maintain quality through instances of user attrition and incomplete calls. Additionally, these subsystems are designed with the foresight to allow for future re-analysis using improved predictive models, as well as enable the creation …

Integrated Machine Learning And Deep Learning Models For Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran

Electrical & Computer Engineering Faculty Publications

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the …

Epileptic Seizure Classification Using Image-Based Data Representation, Amber Surles

Graduate Theses and Dissertations (2019 - present)

Epilepsy is a recurrence of seizures caused by a disorder of the brain in over 3.4 million people nationwide. Some people are able to predict their seizures based off prodrome, which is an early sign or symptom that usually resembles mood changes or a euphoric feeling even days to an hour before occurrence. Consequently, the natural instincts of the body to react to an upcoming attack lends credence to the existence of a pre-ictal state that precedes seizure episodes. Physicians and researchers have thus sought for an automated approach for predicting or detecting seizures.

In this research, we evaluate the …

Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa

Journal of Dentistry Indonesia

Image representation via machine learning is an approach to quantitatively represent histopathological images of head and neck tumors for future applications of artificial intelligence-assisted pathological diagnosis systems. Objective: This study compares image representations produced by a pre-trained convolutional neural network (VGG16) to those produced by a vision transformer (ViT-L/14) in terms of the classification performance of head and neck tumors. Methods: W hole-slide images of five oral t umor categories (n = 319 cases) were analyzed. Image patches were created from manually annotated regions at 4096, 2048, and 1024 pixels and rescaled to 256 pixels. Image representations were …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma

Graduate Theses/Dissertations

Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. The effectiveness of deep learning models are hampered by limited data sets, skewed class distributions, and the undesirable "black box" of neural networks, which decreases their understandability and usability in precision medicine applications. This thesis addresses the challenge of building an explainable deep learning model for a clinical application: predicting the severity of Alzheimer's disease (AD). AD …

Investigating Visinin-Like Protein 1 And Ubiquitinated Visinin-Like Protein 1 As Mild Traumatic Brain Injury Biomarkers, Yueming Wu

Theses and Dissertations--Chemistry

Traumatic brain injury (TBI) continues to be a significant cause of morbidity and mortality worldwide. Despite significant progress in understanding the complex pathophysiology of TBI, the underlying mechanisms remain poorly understood. The primary brain damage is acute and irreversible. However, secondary brain injuries often develop gradually over months to years, creating an opportunity for critical therapeutic interventions. In the past decade, research on TBI biomarkers has seen significant progress. This progress has been driven by the diverse nature of TBI pathologies and the challenges they present for evaluation, management, and prognosis. TBI biomarker proteins resulting from axonal, neuronal, or glial …

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 …

A Human Oral Fluid Assay For D- And L- Isomer Detection Of Amphetamine And Methamphetamine Using Liquid-Liquid Extraction, Brian Robbins, Rob E. Carpenter, Mary Long, Jacob Perry

Human Resource Development Faculty Publications and Presentations

Medical providers are increasingly confronted with clinical decision-making that involves (meth)amphetamines. And clinical laboratories need a sensitive, efficient assay for routine assessment of D- and L-isomers to determine the probable source of these potentially illicit analytes. This paper presents a validated method of D- and L-isomer detection in human oral fluid from an extract used for determination of a large oral fluid assay (63 analytes) on an older AB SCIEX 4000 instrument. Taken from the positive extract, D- and L-analytes were added. The method for extraction included addition of internal standard and a 2-step …

Monte Carlo Simulation Of Thallium-Bromide Semiconductor Detector For Range Verification Of A Carbon Ion Radiotherapy Beam Through Prompt Gamma-Ray Detection, Peter Dimpfl

UNLV Theses, Dissertations, Professional Papers, and Capstones

Thallium-Bromide is a semiconductor material which is well suited for photon detection. Composed of high atomic number elements and a high mass density of 7.56 g/cm3, TlBr has superb photon stopping power. External beam radiation therapy is an ever-changing environment with technology rapidly improving. With proton therapy facilities becoming more widely available, and carbon ion facilities showcasing the benefits of carbon ion radiation therapy, there is an industry need toprovide accurate dosimetric treatment plans to verify patients are receiving proper care. Current clinical practices apply safety margins to clinical target volumes to safe treatment. Through research to improve detection techniques, …

Spectroscopic Measurements Of Meibum Compositional, Structural, And Functional Relationships To Elucidate The Role Of Meibum In Dry Eye., Anthony Chigozie Ewurum

Electronic Theses and Dissertations

The major aim of my dissertation was to investigate the etiology of dry eye disease which affects about 7 million people in the United States, causing symptoms that can lead to visual disturbance. Correlation between dry eye and an abnormal lipid layer of the tear film has been found. Tear film lipids originate mostly from the meibomian glands. Cholesteryl ester (CE) and Wax ester (WE) lipids make up most of the human meibum lipidome and the CE/WE ratio has been shown to decrease in patients with meibomian gland dysfunction. Model studies using synthetic CE and WE, although providing some insight, …

Use Of Machine Learning Approaches And Statistical Techniques To Adjust For Nonadherence In Randomized Clinical Trials., Andrew G Chapple

School of Public Health Faculty Publications

No abstract provided.

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 …

Development Of A New Spe Uplc–Ms/Ms Method For Extraction And Quantitation Of Toluene Diamine On Gloves Following Toluene Diisocyanate Exposure, Asma Daoud, Pierre-Luc Cloutier, Sébastien Gagné, Livain Breau, Jacques Lesage

Études primaires

Rationale

Toluene diisocyanate (TDI) is a highly reactive isocyanate commonly used as a mixture of 2,4- and 2,6- isomers in the production of flexible foams. Exposure to TDI occurs primarily through vapour inhalation in workplaces where TDI is produced or used, but dermal exposure is also possible during some tasks. To ensure workplace safety, accurate monitoring of TDI and toluene diamine (TDA) levels is required. Methods of quantifying field effectiveness of gloves in preventing dermal exposure have not been established. Therefore, there is a need to develop a new practical method for assessing glove effectiveness for TDI/TDA.

Method

A new …

Quantitative Raman Analysis Of Carotenoid Protein Complexes In Aqueous Solution, Joy Udensi, Ekaterina Loskutova, James Loughman, Hugh Byrne

Datasets

Carotenoids are naturally abundant fat-soluble pigmented compounds, with dietary, antioxidant and vision protection advantages. The dietary carotenoids, Beta Carotene, Lutein and Zeaxanthin, complexed with in bovine serum albumin (BSA) in aqueous solution, were explored using Raman spectroscopy to differentiate and quantify their spectral signatures. UV visible absorption spectroscopy was employed to confirm the linearity of responses over the concentration range employed (0.05-1mg/ml) and, of the 4 source wavelengths, 785nm, 660nm, 532nm, 473nm, 532nm was chosen to provide the optimal response. After preprocessing to remove water and BSA contributions, and correct for self-absorption, a partial least squares model with R^{2 …}

Determining States Of Movement In Humans Using Minimally Processed Eeg Signals And Various Classification Methods, Maurice Barnett

All Theses

Electroencephalography (EEG) is a non-invasive technique used in both clinical and research settings to record neuronal signaling in the brain. The location of an EEG signal as well as the frequencies at which its neuronal constituents fire correlate with behavioral tasks, including discrete states of motor activity. Due to the number of channels and fine temporal resolution of EEG, a dense, high-dimensional dataset is collected. Transcranial direct current stimulation (tDCS) is a treatment that has been suggested to improve motor functions of Parkinson’s disease and chronic stroke patients when stimulation occurs during a motor task. tDCS is commonly administered without …

From Mathematics To Medicine: A Practical Primer On Topological Data Analysis (Tda) And The Development Of Related Analytic Tools For The Functional Discovery Of Latent Structure In Fmri Data, Andrew Salch, Adam Regalski, Hassan Abdallah, Raviteja Suryadevara, Michael J. Catanzaro, Vaibhav A. Diwadkar

Mathematics Faculty Research Publications

fMRI is the preeminent method for collecting signals from the human brain in vivo, for using these signals in the service of functional discovery, and relating these discoveries to anatomical structure. Numerous computational and mathematical techniques have been deployed to extract information from the fMRI signal. Yet, the application of Topological Data Analyses (TDA) remain limited to certain sub-areas such as connectomics (that is, with summarized versions of fMRI data). While connectomics is a natural and important area of application of TDA, applications of TDA in the service of extracting structure from the (non-summarized) fMRI data itself are heretofore nonexistent. …

Optimal Analytical Methods For High Accuracy Cardiac Disease Classification And Treatment Based On Ecg Data, Jianwei Zheng

Computational and Data Sciences (PhD) Dissertations

This work constitutes six projects. In the first project, a newly inaugurated research database for 12-lead electrocardiogram signals was created under the auspices of Chapman University and Shaoxing People's Hospital (Shaoxing Hospital Zhejiang University School of Medicine). This database aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. In the second project, we created a new 12-lead ECG database under the auspices of Chapman University and Ningbo First Hospital of Zhejiang University that aims to provide high quality data enabling detection of the distinctions between idiopathic ventricular arrhythmia from right ventricular outflow tract …

C37: Evaluation Of The Diagnostic Efficacy Of Saliva For Detection Of Dengue Antibodies- An Elisa Study, Vidya Gurram Shankar

Annual Research Symposium

No abstract provided.

Adaptive Physics-Based Non-Rigid Registration For Immersive Image-Guided Neuronavigation Systems, Fotis Drakopoulos, Christos Tsolakis, Angelos Angelopoulos, Yixun Liu, Chengjun Yao, Kyriaki Rafailia Kavazidi, Nikolaos Foroglou, Andrey Fedorov, Sarah Frisken, Ron Kikinis, Alexandra Golby, Nikos Chrisochoides

Computer Science Faculty Publications

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A …

Impact Of Hemodynamic Vortex Spatial And Temporal Characteristics On Analysis Of Intracranial Aneurysms, Kevin W. Sunderland

Dissertations, Master's Theses and Master's Reports

Subarachnoid hemorrhage is a potentially devastating pathological condition in which bleeding occurs into the space surrounding the brain. One of the prominent sources of subarachnoid hemorrhage are intracranial aneurysms (IA): degenerative, irregular expansions of area(s) of the cerebral vasculature. In the event of IA rupture, the resultant subarachnoid hemorrhage ends in patient mortality occurring in ~50% of cases, with survivors enduring significant neurological damage with physical or cognitive impairment. The seriousness of IA rupture drives a degree of clinical interest in understanding these conditions that promote both the development and possible rupture of the vascular malformations. Current metrics for the …

Tri-Molybdenum Phosphide (Mo3P) And Multi-Walled Carbon Nanotube Junctions For Volatile Organic Compounds (Vocs) Detection, Baleeswaraiah Muchharla, Praveen Malali, Brenna Daniel, Alireza Kondori, Mohammad Asadi, Wei Cao, Hani E. Elsayed-Ali, Mickaël Castro, Mehran Elahi, Adetayo Adedeji, Kishor Kumar Sadasivuni, Muni Raj Mauya, Kapil Kumar, Abdennaceur Karoui, Bijandra Kumar

Electrical & Computer Engineering Faculty Publications

Detection and analysis of volatile organic compounds’ (VOCs) biomarkers lead to improvement in healthcare diagnosis and other applications such as chemical threat detection and food quality control. Here, we report on tri-molybdenum phosphide (Mo₃P) and multi- walled carbon nanotube (MWCNT) junction-based vapor quantum resistive sensors (vQRSs), which exhibit more than one order of magni- tude higher sensitivity and superior selectivity for biomarkers in comparison to pristine MWCNT junctions based vQRSs. Transmission electron microscope/scanning tunneling electron microscope with energy dispersive x-ray spectroscopy, x-ray diffraction, and x-ray photo- electron spectroscopy studies reveal the crystallinity and the presence of Mo and …

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 …

Nebulizer-Based Systems To Improve Pharmaceutical Aerosol Delivery To The Lungs, Benjamin M. Spence

Theses and Dissertations

Combining vibrating mesh nebulizers with additional new technologies leads to substantial improvements in pharmaceutical aerosol delivery to the lungs across therapeutic administration methods. In this dissertation, streamlined components, aerosol administration synchronization, and/or Excipient Enhanced Growth (EEG) technologies were utilized to develop and test several novel devices and aerosol delivery systems. The first focus of this work was to improve the poor delivery efficiency, e.g., 3.6% of nominal dose (Dugernier et al. 2017), of aerosolized medication administration to adult human subjects concurrent with high flow nasal cannula (HFNC) therapy, a form of continuous-flow non-invasive ventilation (NIV). The developed Low-Volume Mixer-Heater (LVMH) …

Adaptive Background Correction Of Crystal Image Datasets: Towards Automated Process Control, Luke Kiernan, Ian Jones, Lauri Kurki, Patrick J. Cullen, Toufic El Arnaout

Articles

Improving the data descriptor calculation of crystal’s physical properties requires sophisticated imaging techniques and algorithms. It has been possible to construct 2D population balance models benefiting from characteristic measurements of both crystal’s length and width, compared to the single representative sizes used in 1D models. Our aim is to ameliorate the procedure of determining shape (and not only size) factors, in an automated fashion and directly from the process, for implementation in future models. Here, approaches suitable for real-time applications were employed including engineered imaging sensors and adaptive algorithms. We described the latter in detail for varying 2D image datasets. …

Mechanisms Of Value-Biased Prioritization In Fast Sensorimotor Decision Making, Kivilcim Afacan-Seref

Dissertations and Theses

In dynamic environments, split-second sensorimotor decisions must be prioritized according to potential payoffs to maximize overall rewards. The impact of relative value on deliberative perceptual judgments has been examined extensively, but relatively little is known about value-biasing mechanisms in the common situation where physical evidence is strong but the time to act is severely limited. This research examines the behavioral and electrophysiological indices of how value biases split-second perceptual decisions and the possible mechanisms underlying the process. In prominent decision models, a noisy but statistically stationary representation of sensory evidence is integrated over time to an action-triggering bound, and value-biases …

I Spy With My Little Eye … A Knee About To Go 'Pop'? Can Coaches And Sports Medicine Professionals Predict Who Is At Greater Risk Of Acl Rupture?, Anne Inger Mørtvedt, Tron Krosshaug, Roald Bahr, Erich Petushek

Michigan Tech Publications, Part 1

BACKGROUND: The vertical drop jump (VDJ) test is widely used for clinical assessment of ACL injury risk, but it is not clear whether such assessments are valid.

AIM: To examine if sports medicine professionals and coaches are able to identify players at risk of sustaining an ACL injury by visually assessing player performance during a VDJ test.

METHODS: 102 video clips of elite female handball and football players performing a baseline VDJ test were randomly extracted from a 738-person prospective cohort study that tracked ACL injuries. Of the sample, 20 of 102 went on to suffer an ACL injury. These …

Development Of A Screening Method For Drugs Of Abuse By Direct Analysis Of Dried Urine Spots Coupled To Mass Spectrometry, Melanie Goldstein

Student Theses

Fast and easy screening procedures are essential in any forensic toxicology laboratory to differentiate negative samples from presumptive positive cases. Immunoassay techniques, such as enzyme multiplied immunoassay technique (EMIT), are routinely employed as screening procedures. However, these techniques lack specificity (only differentiate group of drugs and not individual compounds) and it is difficult to add new compounds to the panel. Direct analysis of dried urine spots (DUS) by mass spectrometry (MS) offers a novel strategy to overcome these issues. DUS offer an improved storage alternative for biological samples, reducing costs and space requirements. In this work, an original method to …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

Crystal Structure Of Zymonic Acid And A Redetermination Of Its Precursor, Pyruvic Acid, Dominik Heger, Alexis J. Eugene, Sean R. Parkin, Marcelo I. Guzman

Chemistry Faculty Publications

The structure of zymonic acid (systematic name: 4-hy­droxy-2-methyl-5-oxo-2,5-di­hydro­furan-2-carb­oxy­lic acid), C₆H₆O₅, which had previously eluded crystallographic determination, is presented here for the first time. It forms by intra­molecular condensation of parapyruvic acid, which is the product of aldol condensation of pyruvic acid. A redetermination of the crystal structure of pyruvic acid (systematic name: 2-oxo­propanoic acid), C₃H₄O₃, at low temperature (90 K) and with increased precision, is also presented [for the previous structure, see: Harata et al. (1977). Acta Cryst. B33, 210–212]. In zymonic acid, the hy­droxy­lactone ring …