Engineering Commons^™

Correlation Enhanced Distribution Adaptation For Prediction Of Fall Risk, Ziqi Guo, Teresa Wu, Thurmon Lockhart, Rahul Soangra, Hyunsoo Yoon

Physical Therapy Faculty Articles and Research

With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different …

Quantification Of Antiviral Drug Tenofovir (Tfv) By Surface-Enhanced Raman Spectroscopy (Sers) Using Cumulative Distribution Functions (Cdfs), Marguerite R. Butler, Jana Hrncirova, Meredith Clark, Sucharita Dutta, John B. Cooper

Chemistry & Biochemistry Faculty Publications

Surface-enhanced Raman spectroscopy (SERS) is an ultrasensitive spectroscopic technique that generates signal-enhanced fingerprint vibrational spectra of small molecules. However, without rigorous control of SERS substrate active sites, geometry, surface area, or surface functionality, SERS is notoriously irreproducible, complicating the consistent quantitative analysis of small molecules. While evaporatively prepared samples yield significant SERS enhancement resulting in lower detection limits, the distribution of these enhancements along the SERS surface is inherently stochastic. Acquiring spatially resolved SERS spectra of these dried surfaces, we have shown that this enhancement is governed by a power law as a function of analyte concentration. Consequently, by definition, …

Compton Scattering Of Mammographic Soft X-Ray Beams By Alkali And Transition Metal Salt Filters Produce X-Ray Interference Zones That May Have Treatment Potential For Localized Cancer Lesions, Subhendra N. Sarkar, Eric Lobel, Sabina Rakhmatova, Derbie Desir, Somdat Kissoon, Daler Djuraev, Katie Tam

Publications and Research

In breast x-ray imaging scattered radiation adds 50% of harmful radiation dose from anisotropic Compton scattering mechanism. We have been working with double layered inorganic salt materials that can induce Compton scattering to the incident mammographic x ray beams (in 20-30 kVp range) with adequate isotropy (angular control). Typically metal nitrates and alkali halide salt layers are shown here to cause low energy radiation interference zones with high and low photon intensities and local flux heterogeneity in terms of flux covariance. Spatial variation of low energy photon flux creates concentrated and sparse radiation zones that may be used to induce …

An Investigation Of Match For Lossless Video Compression, Brittany Sullivan-Reicks

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

A new lossless video compression technique, Match, is investigated. Match uses the similarity between the frames of a video or the slices of medical images to find a prediction for the current pixel. A portion of the previous frame is searched to find a matching context, which is the pixels surrounding the current pixel, within some distance centered on the current location. The best distance to use for each dataset is found experimentally. The matching context refers to the neighborhood of w, nw, n, and ne, where the pixel in the previous frame with the closest matching context becomes the …

Utilizing The System Engineering Trade Study Analysis Method To Analyze Patient Aeromedical Evacuation, Sara Shaghaghi, Jeremy M. Slagley, Michael E. Miller, Gaiven Varshney

Faculty Publications

The US Air Force has gone through many aeromedical patient isolation transport system designs. The first designs were developed in response to the Ebola outbreak in 2014 and, more recently, the COVID-19 pandemic. The trade study analysis part of the system engineering design method was used to analyze the historic and current aeromedical patient contamination control transport systems. A trade study is a process that evaluates alternatives based upon various “-ilities”, such as reconfigurability, flexibility, durability, cost, and more, and performs a systematic analysis to aid designers in producing a ‘good’ design alternative given the large set of possible solutions. …

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 …

Efficient Training On Alzheimer’S Disease Diagnosis With Learnable Weighted Pooling For 3d Pet Brain Image Classification, Xin Xing, Muhammad Usman Rafique, Gongbo Liang, Hunter Blanton, Zu Zhang, Chris Wang, Nathan Jacobs, Ai-Ling Lin

Computer Science Faculty Publications

Three-dimensional convolutional neural networks (3D CNNs) have been widely applied to analyze Alzheimer’s disease (AD) brain images for a better understanding of the disease progress or predicting the conversion from cognitively impaired (CU) or mild cognitive impairment status. It is well-known that training 3D-CNN is computationally expensive and with the potential of overfitting due to the small sample size available in the medical imaging field. Here we proposed a novel 3D-2D approach by converting a 3D brain image to a 2D fused image using a Learnable Weighted Pooling (LWP) method to improve efficient training and maintain comparable model performance. By …

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 …

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 …

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, …

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 …

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 …

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 …

Effect Of Injury Mechanism And Severity On The Molecular Pathophysiology Of Traumatic Brain Injury, Brandon Mcdonald

Department of Biological Systems Engineering: Dissertations and Theses

Traumatic brain injury (TBI) mechanism and severity are heterogenous clinically, resulting in a multitude of physical, cognitive, and behavioral deficits. However, approximately 80% suffer from milder injuries. Thus, examining pathophysiological changes associated with mild TBI is imperative for improving clinical translation and evaluating the efficacy of potential therapeutic strategies. Through this work, we developed models of TBI, ranging in both injury mechanism and severity, using an electromagnetic controlled cortical impact (CCI) device. First, we characterized and optimized a closed head, mild TBI model (DTBI) to determine the clinical translatability and practicality of producing repeated mild injuries. Interestingly, we determined that …

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 …

Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead

Engineering Faculty Articles and Research

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of …

Toward A Multimodal Computer-Aided Diagnostic Tool For Alzheimer’S Disease Conversion, Danilo Pena, Jessika Suescun, Mya Schiess, Timothy M. Ellmore, Luca Giancardo, Alzheimer’S Disease Neuroimaging Initiative

Publications and Research

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current …

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 …

Deep Learning Approach To Improved Image Quality For Medical Diagnostics, Olivia Loesch, Katie Leyba, Halyley Chan, Craig Goergen

Discovery Undergraduate Interdisciplinary Research Internship

The United Nation’s health-related Sustainable Development Goals are difficult to achieve in low- and middle-income countries due to workforce shortages and inadequate health surveillance systems. However, with the growth of artificial intelligence (AI) and computer algorithms, it is possible to apply AI to healthcare technologies to improve progress towards these UN standards. This project aims at using and improving computer algorithms and deep learning to aid in the extraction of important structural and functional information from murine carotid artery ultrasound and photoacoustic images. First, we created a large database of simulated photoacoustic images to optimize the algorithms. These images were …

Aptamer-Based Voltammetric Biosensing For The Detection Of Codeine And Fentanyl In Sweat And Saliva, Rosa Lashantez Cromartie

FIU Electronic Theses and Dissertations

Despite the many governmental and medicinal restrictions created to combat the opioid epidemic in the United States, opioid abuse and overdose rates continue to rise. The development of an aptamer-based voltammetric sensor and biosensor is described in this dissertation. The aim was to develop a low-cost, sensitive, and specific aptamer-based sensor for on-site, label-free determination of codeine and fentanyl in biological fluids. To do this, the surfaces of screen-printed carbon electrodes (SPCE) were modified with gold nanoparticles (AuNPs), followed by the addition of single-stranded DNA aptamers. These were covalently bound to the electrode surface. Operations of the sensors were collected …

Detection Methods And Clinical Applications Of Circulating Tumor Cells In Breast Cancer, Hongyi Zhang, Xiaoyan Lin, Yuan Huang, Minghong Wang, Chunmei Cen, Shasha Tang, Marcia R. Dique, Lu Cai, Manuel A. Luis, Jillian Smollar, Yuan Wan, Fengfeng Cai

Publications and Research

Circulating Tumor Cells (CTCs) are cancer cells that split away from the primary tumor and appear in the circulatory system as singular units or clusters, which was first reported by Dr. Thomas Ashworth in 1869. CTCs migrate and implantation occurs at a new site, in a process commonly known as tumor metastasis. In the case of breast cancer, the tumor cells often migrate into locations such as the lungs, brain, and bones, even during the early stages, and this is a notable characteristic of breast cancer. Survival rates have increased significantly over the past few decades because of progress made …

Test-Retest Reliability And Minimal Detectable Change Of The Computerized Dynamic Posturography Proprio For Adults With Chronic Traumatic Brain Injury, Guilherme Manna Cesar, Thad W. Buster, Judith M. Burnfield

Department of Mechanical and Materials Engineering: Faculty Publications

Purpose: Balance deficits after brain injury, including reactive recovery from unexpected perturbations, can persist well after rehabilitation is concluded. While traditional clinical assessments are practical, the anticipatory nature of the tasks may mask perceptible balance control. Computerized dynamic posturography can directly quantify capacity to respond to unexpected, external perturbations. This study examined the reliability of the computerized dynamic posturography assessment with the device PROPRIO® 4000 in adults with traumatic brain injury and created the minimal detectable change for its standardized test.

Methods: Ten adults (ages 21–55 years) with chronic (average 10 ± 6 years post-injury) severe (loss of consciousness 2–75 …

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 …

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 …

Feasibility Of Continuous Fever Monitoring Using Wearable Devices, Benjamin L. Smarr, Kirstin Aschbacher, Sarah M. Fisher, Anoushka Chowdhary, Stephan Dilchert, Karena Puldon, Adam Rao, Frederick M. Hecht, Ashley E. Mason

Publications and Research

Elevated core temperature constitutes an important biomarker for COVID-19 infection; however, no standards currently exist to monitor fever using wearable peripheral temperature sensors. Evidence that sensors could be used to develop fever monitoring capabilities would enable large-scale health-monitoring research and provide high-temporal resolution data on fever responses across heterogeneous populations. We launched the TemPredict study in March of 2020 to capture continuous physiological data, including peripheral temperature, from a commercially available wearable device during the novel coronavirus pandemic. We coupled these data with symptom reports and COVID-19 diagnosis data. Here we report findings from the first 50 subjects who reported …

3d Reconstruction Of Spine Image From 2d Mri Slices Along One Axis, Somoballi Ghoshal, Sourav Banu, Amlan Chakrabarti, Susmita Sur-Kolay, Alok Pandit

Journal Articles

Magnetic resonance imaging (MRI) is a very effective method for identifying any abnormality in the structure and physiology of the spine. However, MRI is time consuming as well as costly. In this work, the authors propose an algorithm which can reduce the time of MRI and thus the cost, with minimal compromise on accuracy. They reconstruct a three-dimensional (3D) image of the spine from a sequence of 2D MRI slices along any one axis with reasonable slice gap. In order to preserve the image at the edges properly, they regenerate the 3D image by using a combination of bicubic and …

Machine Learning Approaches For Fracture Risk Assessment: A Comparative Analysis Of Genomic And Phenotypic Data In 5130 Older Men, Qing Wu, Fatma Nasoz, Jongyun Jung, Bibek Bhattarai, Mira V. Han

Public Health Faculty Publications

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n = 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures …

Face Mask Effects Of Co2, Heart Rate, Respiration Rate, And Oxygen Saturation On Instructor Pilots, Andrew R. Dattel, Nicola M. O'Toole, Guillermina Lopez, Kenneth P. Byrnes

Publications

The COVID-19 pandemic has required people to take new measures to mitigate the spread of the communicable virus. Guidelines from health organizations, government offices, and universities have been disseminated. Adherence to these guidelines cannot be more critical for flight training. This study explored the effects face masks had on CO2, heart rate, respiration rate, and oxygen saturation while wearing a face mask at an oxygen level simulated to 5,000 feet. Thirty-two instructor pilots (IP) volunteered to participate in the study. IPs spent 90 minutes in a normobaric chamber while wearing a cloth face mask or a paper face mask. Participants …

Development Of Gaussian Learning Algorithms For Early Detection Of Alzheimer's Disease, Chen Fang

FIU Electronic Theses and Dissertations

Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer's Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot …

Cardiorespiratory Fitness, Balance And Walking Improvements In An Adolescent With Cerebral Palsy (Gmfcs Ii) And Autism After Motor-Assisted Elliptical Training, Guilherme Manna Cesar, Thad W. Buster, Judith M. Burnfield

Department of Mechanical and Materials Engineering: Faculty Publications

Purpose: To quantify the impact of motor-assisted elliptical (ICARE) training on cardiorespiratory fitness, balance and walking function of an adolescent with walking limitations due to cerebral palsy.

Materials and methods: A thirteen-year-old boy with hemiplegic cerebral palsy (Gross Motor Function Classification System II) and autism participated. Peak oxygen consumption (peak VO₂, primary outcome measure), oxygen cost of walking, Pediatric Balance Scale (PBS), modified Timed Up and Go (mTUG), 2-Minute Walk Test (2MWT), and gait characteristics (speed, cadence, step length, single support time) were assessed prior to and after completion of 24 sessions of moderate- to vigorous- intensity ICARE …