Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

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 …

Deutj: An Imagej Plugin For Improved Automatic Masking And Segmentation Of Images From Confocal Microscopy, Sunny Cui

Independent Student Projects and Publications

Due to advances in microscopic imaging, there are a plethora of biological molecules that can now be tagged and subsequentially imaged from almost any cell, organism, or tissue. However, the ability of software to analyze these images remains to be a challenge. ImageJ is open source software that allows for the processing of these images, but faces challenges when dealing with images that show weak contrast between objects of interest and background. DeutJ is an ImageJ plugin that color corrects and brightness corrects gradients that inhibit segmentation in confocal microscopy images. It can analyze hundreds of images from a given …

Session 12: Active Learning To Minimize The Possible Risk From Future Epidemics, Kc Santosh

SDSU Data Science Symposium

In medical imaging informatics, for any future epidemics (e.g., Covid-19), deep learning (DL) models are of no use as they require a large dataset as they take months and even years to collect enough data (with annotations). In such a context, active learning (or human/expert-in-the-loop) is the must, where a machine can learn from the first day with minimum possible labeled data. In unsupervised learning, we propose to build pre-trained DL models that iteratively learn independently over time, where human/expert intervenes only when it makes mistakes and for only a limited data. In our work, deep features are used to …

Electrochemical Sensors And Biosensors Of Biomarkers And Therapeutic Agents Detection, Mahmoud Abdelhakem

Theses and Dissertations

Electrochemical sensing is a pioneering tool amongst all other sensing techniques even though it had been started in the early 20th century. Due to their cost efficacy, accuracy, and real-time measurement, electrochemical sensing has been utilized in a vast of biomedical applications in therapeutic drug monitoring and biomarker detection. This thesis work concerned the monitoring of three therapeutic agents in spiked plasma, urine, dosage form, and interstitial skin fluid with high specificity and accuracy. Two of our sensing platforms utilized nanomaterials for sensing (sensors) of Cilostazol and Velpatasvir utilizing α-MnO2-V2O5 (metal oxide nanorods) and NH2- MIL-53 (Aluminum-based metal organic frameworks), …

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 …

Modeling Of The Chest Wall Response To Prolonged Bracing In Pectus Carinatum, Brandon Sargent, Katie Varela, Dennis Eggett, Emily Mckenna, Christina Bates, Rebeccah Brown, Victor Garcia, Larry Howell

Biomedical Engineering Faculty Scholarship

Pectus carinatum is a chest wall deformity that is often treated through the wearing of an external brace. The treatment of the deformity could benefit from a greater understanding of chest wall characteristics under prolonged loading. These characteristics are difficult to model directly but empirical studies can be used to create statistical models. 185 patients from 2018-2020 received bracing treatment. Data on the severity of the deformity, treatment pressures, and time of wear were recorded at the first fitting and all subsequent follow-up visits. This data was analyzed using a statistical mixed effects model to identify significant measures and trends …

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 (Archived)

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 …

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

Bioelectrics Publications

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

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

School of Cybersecurity Faculty Publications

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

Brain Activity Associated With Taste Stimulation: A Mechanism For Neuroplastic Change?, Angela M. Dietsch, Ross M. Westemeyer, Douglas H. Schultz

Department of Special Education and Communication Disorders: Faculty Publications

Purpose: Neuroplasticity may be enhanced by increasing brain activation and bloodflow in neural regions relevant to the target behavior.We administered precisely formulated and dosed taste stimuli to determine whether the associated brain activity patterns included areas that underlie swallowing control.

Methods: Five taste stimuli (unflavored, sour, sweet-sour, lemon, and orange suspensions) were administered in timing-regulated and temperature-controlled 3 mL doses via a customized pump/tubing system to 21 healthy adults during functional magnetic resonance imaging (fMRI). Whole-brain analyses of fMRI data assessed main effects of taste stimulation as well as differential effects of taste profile.

Results: Differences in …

Use Of Bioheat Modeling To Characterize And Optimize Implantable Medical Devices And Neuromodulation Technologies, Adantchede Louis Zannou

Dissertations and Theses

Medical device development includes prototyping, benchtop characterization, preclinical studies, and clinical trials. Understanding the limitations and potential adverse effects of medical devices prior to their administration in humans is a crucial first step. Optimizing medical devices is essential to employing technology and improving patients care. Computational modeling is widely adopted as a powerful tool to predict stimulation/recording parameter optimization, rapid electrode/device prototyping, investigating novel mechanism of action, and testing working principles of any medical devices. Many implantable neuromodulation technologies including Spinal Cord Stimulation (SCS), which provide substantial therapeutic benefit for patient population with lower back pain, produces heat via the …

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

Electrical & Computer Engineering Faculty Publications

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

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

Electrical & Computer Engineering Faculty Publications

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

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 …

Infrastructure Development For Personalized Risk Prediction To Reduce Cardiovascular Disease In Childhood Cancer Survivors, Suman Shrestha

Dissertations and Theses (Open Access)

Although childhood cancer survivors have lengthy life expectancies, they run the risk of experiencing long-term health issues as a result of their treatment. The most frequent non-cancerous cause of morbidity and mortality for these survivors is cardiac disease. Radiation therapy (RT) has been linked in numerous cohort studies to a higher chance of developing a late cardiac disease in these survivors, and this risk rises with higher mean heart doses and increased RT exposure to larger cardiac volumes. Since, the heart is a heterogeneous organ made up of several distinct substructures, RT dose received by the entire heart does not …

Seeing The Big Picture: System Architecture Trends In Endoscopy And Led-Based Hyperspectral Subsystem Intergration, Craig M. Browning

Graduate Theses and Dissertations (2019 - present)

Early-stage colorectal lesions remain difficult to detect. Early development of neoplasia tends to be small (less than 10 mm) and flat and difficult to distinguish from surrounding mucosa. Additionally, optical diagnosis of neoplasia as benign or malignant is problematic. Low rates of detection of these lesions allow for continued growth in the colorectum and increased risk of cancer formation. Therefore, it is crucial to detect neoplasia and other non-neoplastic lesions to determine risk and guide future treatment. Technology for detection needs to enhance contrast of subtle tissue differences in the colorectum and track multiple biomarkers simultaneously. This work implements one …

A Study Of Heart Disease Diagnosis Using Machine Learning And Data Mining, Intisar Ahmed

Electronic Theses, Projects, and Dissertations

Heart disease is the leading cause of death for people around the world today. Diagnosis for various forms of heart disease can be detected with numerous medical tests, however, predicting heart disease without such tests is very difficult. Machine learning can help process medical big data and provide hidden knowledge which otherwise would not be possible with the naked eye. The aim of this project is to explore how machine learning algorithms can be used in predicting heart disease by building an optimized model. The research questions are; 1) What Machine learning algorithms are used in the diagnosis of heart …

Mesures À Haute Sensibilité Du Passage De Contaminants Nanométriques À Travers Les Gants De Protection Par Imagerie Médicale, Mahmoud Mohamed Omar, Amélie Augé, Myriam Laprise-Pelletier, Ludovic Tuduri, Marc-André Fortin

Rapports de recherche scientifique

Les gants de polymère sont utilisés comme équipements de protection individuels dans de nombreux secteurs d’activité professionnelle où les risques chimiques sont présents. Leur efficacité à bloquer le passage de certains contaminants doit être mesurée par des méthodes d’essais précises et standardisées. Les mesures de résistance chimique sont généralement effectuées en utilisant une cellule de perméation, un petit appareil contenant deux réservoirs séparés par la membrane dont on veut mesurer le potentiel-barrière : l’un contient le contaminant, l’autre contient un milieu collectant le contaminant après que celui-ci ait passé à travers la membrane. Au fur et à mesure de l’expérience, …

Machine Learning And Scalable Informatics Methods To Predict Disease Status From Multimodal Biomedical Data, Hossein Mohammadian Foroushani

McKelvey School of Engineering Graduate Student Theses & Dissertations

Biological understanding of complex diseases such as stroke and obesity is critical for the advancement of medicine. Further knowledge discovery can provide effective biomarkers to improve disease diagnosis and prognosis, identify driver mutations, predict individual genetic susceptibility for early prevention and effective disease management, and facilitate development of personalized drugs. Stroke is the second leading cause of death and long-term disability in the world. Thus, stroke management is a time-sensitive emergency. The initial hours after stroke onset map the trajectory of subsequent neurologic complications. Cerebral edema develops hours to days after acute ischemic stroke and may result in midline shift …

Evaluation And Clinical Implementation Of A Dual-Energy Ct Stopping-Power Ratio Mapping Technique For Proton-Therapy Treatment Planning, Maria Jose Medrano Matamoros

McKelvey School of Engineering Graduate Student Theses & Dissertations

Proton radiotherapy has the potential to treat tumors with better conformal dose distribution than competing modalities when the rapid dose falloff at the end of the proton-beam range is correctly aligned to the edge of the clinical target volume (CTV). However, its clinical potential is dependent on the accurate localization of the Bragg-peak position from predicted stopping-power ratio maps. The method that is most commonly used in today’s clinical practice for predicting stopping-power ratio (SPR) consists of a stoichiometric calibrationtechnique based on single-energy CT (SECT) for direct estimation of patient-specific SPR distribution from vendor-reconstructed Hounsfield Unit (HU) images. Unfortunately, this …

Advancing The Portability, Field Of View, And Spatiotemporal Resolution Of Multi-Parametric Photoacoustic Microscopy, Fenghe Zhong

McKelvey School of Engineering Graduate Student Theses & Dissertations

Capable of structural, functional, molecular, and metabolic imaging with high spatial resolution in vivo, photoacoustic microscopy (PAM) is an emerging tool in biomedical research. Further, recent advances in multi-parametric acquisition and analysis make PAM uniquely capable of simultaneously mapping the total concentration of hemoglobin (CHb), oxygen saturation of hemoglobin (sO2), and blood flow speed. However, there are multiple major limitations, including, the large footprint, low imaging speed, limited imaging contrast, and insufficient axial resolution, which impedance its applications in basic and translational research. In this dissertation, I presented potential solutions to overcome the limitations above.In the first part of this …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

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

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

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 …

Multicolor Fluorescence Guided Surgery For Head And Neck Cancer, Dani Szafran-Reeder

University Honors Theses

This project aims to take a novel, NIR nerve-specific fluorophore LGW05-75⁴ and test its compatibility with a tumor-targeting affibody molecule, ABY-029⁶ that has been labeled with IRDye800CW. The overall goal is to show that these two molecules can be co-administered to show the nerves and tumor cells in real-time to better assist surgeons in a full resection while avoiding iatrogenic nerve damage.

Nanoparticles For Biomedical Applications, Joseph Kim

Dissertations and Theses (Open Access)

This thesis presents development and evaluation of the potential of three new nanoparticles for biomedical applications. With the rapid growth of the field of nanoscience, researchers have explored developing nanoparticles for various biomedical applications, including imaging, therapy, and drug delivery. This thesis demonstrates the development of two C­₆₀ fullerene based nanoparticles and one boron based nanoparticle to answer key questions related to their biological potential.

In the first part of the thesis, we describe synthesis and characterization of a pure boron nanoparticle containing asolectin phospholipid-based liposome construct prepared using a water-in-oil emulsion method, as a novel alternative agent for …

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 …

Cbct In Clinical Practice, Tarunjeet Pabla Bds, Dmd, Ms, Dip. Abomr, Hugo C. Campos Dds, Dmd, Mds, Dip. Abomr, Aruna Ramesh Bds, Dmd, Ms, Dip. Abomr

The Journal of the Michigan Dental Association

This feature explores the integration of Cone Beam Computed Tomography (CBCT) into dental practice, offering guidelines for best practices. Introduced in 2001, CBCT revolutionized dental radiography, impacting various clinical areas. The article emphasizes the need for clinicians to comprehend CBCT technology, its benefits, and potential risks. It delves into CBCT imaging considerations, technical parameters (Field of View, Voxel Size, Spatial and Contrast Resolution), image viewing, artifacts, machine calibration, and service. Addressing radiation dose, risks, and protection, the article outlines decision-making for 2D vs. 3D imaging. It underscores the responsibility of interpreting CBCT images, legal considerations, return on investment, and the …

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 …