Medicine and Health Sciences Commons^™

Ultrashort Pulsed Laser Treatment Is Effective At Sterilizing Metal Surfaces For Planetary Protection, Kaleb Mcquillan

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

To prevent forward contamination from microbes aboard spacecraft intended for exploration of solar system bodies there is a need for effective sterilization methods. However, current techniques are both time-consuming and expensive. For example, dry heat sterilization requires removal from the assembly site and several days of treatment. Furthermore, some components such as optics and electronics are not compatible with current sterilization techniques. In this thesis, a novel femtosecond laser surface processing technique for the rapid sterilization of spacecraft hardware is reported. Femtosecond lasers produce extremely high photon fluxes (10²⁹ photons/s*cm², ~0.03 J/cm²) in extremely short …

Development Of Feature Extraction Models To Improve Image Analysis Applications In Cancer, Yu Shi

Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–

Cancer poses a significant global health challenge. With an estimated 20 million new cases diagnosed worldwide in 2022 and 9.7 million fatalities attributable to the disease, the economic burden of cancer is immense. It impacts healthcare systems and imposes substantial costs for its care on patients and their families. Despite advancements in early detection, prevention, and treatment that have reduced overall cancer mortality rates, the growing prevalence of cancer, particularly among younger individuals, remains a pressing issue.

Recent advancements in medical imaging technology have progressed significantly with the help of emerging computer vision and artificial intelligence (AI) technology. Despite these …

Use Of Mobile Technology To Identify Behavioral Mechanisms Linked To Mental Health Outcomes In Kenya: Protocol For Development And Validation Of A Predictive Model, Willie Njoroge, Rachel Maina, Frank Elena, Lukoye Atwoli, Anthony Ngugi, Srijan Sen, Stephen Wong, Linda Khakali, Andrew Aballa, James Orwa, Moses Nyongesa, Jasmit Shah, Amina Abubakar, Zul Merali

Brain and Mind Institute

Objective:This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya.

Approach: The study will deploy a mobile application (app) platform and use novel data science analytic approaches (Artificial Intelligence and Machine Learning) to identifying predictors of mental health disorders among 500 randomly sampled healthcare workers from five healthcare facilities in Nairobi, Kenya.

Expectation: This study will lay the basis for creating agile and scalable systems for rapid diagnostics that could inform precise interventions for mitigating depression and ensure a healthy, resilient …

Digital Phobia: An Inquiry For Mapping The Unseen Dimension Of New Digital Anxiety, The ‘Digiphobia’, Amarjit Kumar Singh ,Library Assistant, Md. Arshad Ali , Professional Assistant, Dr. Pankaj Mathur, Deputy Librarian,

Library Philosophy and Practice (e-journal)

Background: As technology continues to advance, individuals' interactions with digital platforms have become integral to daily life. Amidst this technological evolution, a novel concern emerges—Digital Phobia, hereafter referred to as “Digiphobia.” This phenomenon, although not previously explored in scholarly literature, necessitates an in-depth investigation due to its potential impact on individuals' well-being. Our research employs a two-step methodology to investigate its existence, implications, and manifestations.

Introduction: This research paper introduces and proposes the term "Digiphobia" as a comprehensive conceptualization of anxiety arising from interactions with digital spaces, applications, and environments. The proliferation of digital technologies has led to the emergence …

Detection Of Tooth Position By Yolov4 And Various Dental Problems Based On Cnn With Bitewing Radiograph, Kuo Chen Li, Yi-Cheng Mao, Mu-Feng Lin, Yi-Qian Li, Chiung-An Chen, Tsung-Yi Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding …

On The Performance Of A Photonic Reconfigurable Electromagnetic Band Gap Antenna Array For 5g Applications, Taha A. Elwi, Fatma Taher, Bal S. Virdee, Mohammad Alibakhshikenari, Ignacio J.Garcia Zuazola, Astrit Krasniqi, Amna Shibib Kamel, Nurhan Turker Tokan, Salahuddin Khan, Naser Ojaroudi Parchin, Patrizia Livreri, Iyad Dayoub, Giovanni Pau, Sonia Aissa, Ernesto Limiti, Mohamed Fathy Abo Sree

All Works

In this paper, a reconfigurable Multiple-Input Multiple-Output (MIMO) antenna array is presented for 5G portable devices. The proposed array consists of four radiating elements and an Electromagnetic Band Gap (EBG) structure. Planar monopole radiating elements are employed in the array with Coplanar Waveguide Ports (CWPs). Each CWP is grounded on one side to a reflecting L-shaped structure that has an effect of improving the antenna's directivity. It is shown that by inductively connecting Minkowski fractal structure of 1^{st} order to the radiating element, the impedance matching is improved that results in enhancement in the array's bandwidth performance. The EBG structure …

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 …

A Dynamic Online Dashboard For Tracking The Performance Of Division 1 Basketball Athletic Performance, Erica Juliano, Chelsea Thakkar, Christopher B. Taber, Mehul S. Raval, Kaya Tolga, Samah Senbel

School of Computer Science & Engineering Undergraduate Publications

Using Data Analytics is a vital part of sport performance enhancement. We collect data from the Division 1 'Women's basketball athletes and coaches at our university, for use in analysis and prediction. Several data sources are used daily and weekly: WHOOP straps, weekly surveys, polar straps, jump analysis, and training session information. In this paper, we present an online dashboard to visually present the data to the athletes and coaches. R shiny was used to develop the platform, with the data stored on the cloud for instant updates of the dashboard as the data becomes available. The performance of athletes …

Feasibility And Outcomes Of Supplemental Gait Training By Robotic And Conventional Means In Acute Stroke Rehabilitation, Mukul Talaty, Alberto Esquenazi

Moss-Magee Rehabilitation Papers

INTRODUCTION: Practicality of implementation and dosing of supplemental gait training in an acute stroke inpatient rehabilitation setting are not well studied but can have positive impact on outcomes.

OBJECTIVES: To determine the feasibility of early, intense supplemental gait training in inpatient stroke rehabilitation, compare functional outcomes and the specific mode of delivery.

DESIGN AND SETTING: Assessor blinded, randomized controlled trial in a tertiary Inpatient Rehabilitation Facility.

PARTICIPANTS: Thirty acute post-stroke patients with unilateral hemiparesis (≥ 18 years of age with a lower limb MAS ≤ 3).

INTERVENTION: Lokomat® or conventional gait training (CGT) in addition to standard mandated therapy time. …

Adaptive Octree Meshes For Simulation Of Extracellular Electrophysiology, Christopher Bc Girard, Dong Song

Engineering Faculty Articles and Research

Objective. The interaction between neural tissues and artificial electrodes is crucial for understanding and advancing neuroscientific research and therapeutic applications. However, accurately modeling this space around the neurons rapidly increases the computational complexity of neural simulations. Approach. This study demonstrates a dynamically adaptive simulation method that greatly accelerates computation by adjusting spatial resolution of the simulation as needed. Use of an octree structure for the mesh, in combination with the admittance method for discretizing conductivity, provides both accurate approximation and ease of modification on-the-fly. Main results. In tests of both local field potential estimation and multi-electrode stimulation, dynamically adapted meshes …

Using Machine Learning To Assist Auditory Processing Evaluation, Hasitha Wimalarathna, Sangamanatha Veeranna, Minh Vu Duong, Chris Allan Prof, Sumit K. Agrawal, Prudence Allen, Jagath Samarabandu, Hanif M. Ladak

Electrical and Computer Engineering Publications

Introduction: Approximately 0.2–5% of school-age children complain of listening difficulties in the absence of hearing loss. These children are often referred to an audiologist for an auditory processing disorder (APD) assessment. Adequate experience and training is necessary to arrive at an accurate diagnosis due to the heterogeneity of the disorder.

Objectives: The main goal of the study was to determine if machine learning (ML) can be used to analyze data from the APD clinical test battery to accurately categorize children with suspected APD into clinical sub-groups, similar to expert labels.

Methods: The study retrospectively collected data from 134 children referred …

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 …

Current Topics In Technology-Enabled Stroke Rehabilitation And Reintegration: A Scoping Review And Content Analysis, Katryna Cisek

Articles

Background. There is a worldwide health crisis stemming from the rising incidence of various debilitating chronic diseases, with stroke as a leading contributor. Chronic stroke management encompasses rehabilitation and reintegration, and can require decades of personalized medicine and care. Information technology (IT) tools have the potential to support individuals managing chronic stroke symptoms. Objectives. This scoping review identifies prevalent topics and concepts in research literature on IT technology for stroke rehabilitation and reintegration, utilizing content analysis, based on topic modelling techniques from natural language processing to identify gaps in this literature. Eligibility Criteria. Our methodological search initially identified over 14,000 …

Forecasting Covid-19 Cases Using Dynamic Time Warping And Incremental Machine Learning Methods, Luis Miralles-Pechuán, Ankit Kumar, Andres L. Suarez-Cetrulo

Articles

The investment of time and resources for developing better strategies is key to dealing with future pandemics. In this work, we recreated the situation of COVID-19 across the year 2020, when the pandemic started spreading worldwide. We conducted experiments to predict the coronavirus cases for the 50 countries with the most cases during 2020. We compared the performance of state-of-the-art machine learning algorithms, such as long-short-term memory networks, against that of online incremental machine learning algorithms. To find the best strategy, we performed experiments to test three different approaches. In the first approach (single-country), we trained each model using data …

Assessing The Impact Of Contact Tracing With An Agent-Based Model For Simulating The Spread Of Covid-19: The Irish Experience, Elizabeth Hunter, Sudipta Saha, Jwenish Kumawat, Ciara Carroll, John Kelleher, Claire Buckley, Conor Mcaloon, Patricia Kearney, Michelle Gilbert, Greg Martin

Articles

Contact tracing is an important tool in managing infectious disease outbreaks and Ireland used a comprehensive contact tracing program to slow the spread of COVID-19. Although the benefits of contact tracing seem obvious, it is difficult to estimate the actual impact contact tracing has on an outbreak because it is hard to separate the effects of contact tracing from other behavioural changes or interventions. To understand the impact contact tracing had in Ireland, we used an agent-based model that is designed to simulate the spread of COVID-19 through Ireland. The model uses real contact tracing data from the first year …

Medical Concept Mention Identification In Social Media Posts Using A Small Number Of Sample References, Vasudevan Nedumpozhimana, Sneha Rautmare, Meegan Gower, Maja Popovic, Nishtha Jain, Patricia Buffini, John Kelleher

Conference papers

Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.

Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

The brain is one of the most important and complex organs in the body, consisting of billions of individual cells. Uncontrolled growth and expansion of aberrant cell populations within or around the brain are the main causes of brain tumors. These cells have the potential to harm healthy cells and impair brain function [1]. Tumors can be detected using medical imaging techniques, which are considered the most popular and accurate way to classify different types of cancer, and this procedure is even more crucial as it is noninvasive [2]. Magnetic resonance imaging (MRI) is one such medical imaging technique that …

Technology Adoption Of Computer-Aided Instruction In Healthcare: A Structured Review, Queenie Kate Cabanilla, Frevy Teofilo-Orencia, Rentor Cafino, Armando T. Isla Jr., Jehan Grace Maglaya, Xavier-Lewis Palmer, Lucas Potter, Dave E. Marcial, Lemuel Clark Velasco

Electrical & Computer Engineering Faculty Publications

Computer-Aided Instruction (CAI) is one of the interactive teaching methods that electronically presents instructional resources and enhances learner performance. In health settings, using CAI is one of the important ways to improve learners' knowledge and usefulness in their healthcare specialization yet there is still a lack of research that offers a comprehensive synthesis of investigating into the adoption of CAI in healthcare. This research aims to provide a comprehensive review of related literatures on the enablers and barriers for technology adoption of CAI in healthcare. 31 journals were analyzed and revealed that several studies were utilizing the Unified Theory of …

Exploring The Impact Of Noise And Degradations On Heart Sound Classification Models, Davoud Shariat Panah, Andrew Hines, Susan Mckeever

Articles

The development of data-driven heart sound classification models has been an active area of research in recent years. To develop such data-driven models in the first place, heart sound signals need to be captured using a signal acquisition device. However, it is almost impossible to capture noise-free heart sound signals due to the presence of internal and external noises in most situations. Such noises and degradations in heart sound signals can potentially reduce the accuracy of data-driven classification models. Although different techniques have been proposed in the literature to address the noise issue, how and to what extent different noise …

Understanding And Predicting Cognitive Improvement Of Young Adults In Ischemic Stroke Rehabilitation Therapy, Helard Becerra Martinez, Katryna Cisek, Alejandro Garcia-Rudolph, John Kelleher, Andrew Hines

Articles

Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected …

Enhancing The Prediction For Shunt‑Dependent Hydrocephalus After Aneurysmal Subarachnoid Hemorrhage Using A Machine Learning Approach, Dietmar Frey, Adam Hilbert, Anton Früh, Vince Istvan Madai, Tabea Kossen, Julia Kiewitz, Jenny Sommerfeld, Peter Vajkoczy, Meike Unteroberdörster, Esra Zihni, Sophie Charlotte Brune, Stefan Wolf, Nora Franziska Dengler

Articles

Early and reliable prediction of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid haemorhage (a SAH) may decrease the duration of in-hospital stay and reduce the risk of catheter-associated meningitis. Machine learning (ML) may improve predictions of SDHC in comparison to traditional non-ML methods. ML models were trained for CHESS and SDASH and two combined individual feature sets with clinical, radiographic, and laboratory variables. Seven different algorithms were used including three types of generalized linear models (GLM) as well as a tree boosting (Cat Boost) algorithm, a Nave Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net. The discrimination of …

Tutorial: Neuro-Symbolic Ai For Mental Healthcare, Kaushik Roy, Usha Lokala, Manas Gaur, Amit Sheth

Publications

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, …

Learning To Automate Follow-Up Question Generation Using Process Knowledge For Depression Triage On Reddit Posts, Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth

Publications

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of `depression', our experiments show that DLMs coupled with process knowledge in a mental health questionnaire …

Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors And Machine Learning Algorithms, Rahul Soangra, Yuxin Wen, Hualin Yang, Marybeth Grant-Beuttler

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait abnormality in which children’s toes touch at initial contact and demonstrate limited or no heel contact throughout the gait cycle. Toe walking results in poor balance, increased risk of falling, and developmental delays among children. Identifying toe walking steps during walking can facilitate targeted intervention among children diagnosed with ITW. With recent advances in wearable sensing, communication technologies, and machine learning, new avenues of managing toe walking behavior among children are feasible. In this study, we investigate the capabilities of Machine Learning (ML) algorithms in identifying initial foot contact (heel strike versus toe …

A Music-Therapy Robotic Platform For Children With Autism: A Pilot Study, Huanghao Feng, Mohammad H. Mahoor, Francesca Dino

Electrical and Computer Engineering: Faculty Scholarship

Children with Autism Spectrum Disorder (ASD) experience deficits in verbal and nonverbal communication skills including motor control, turn-taking, and emotion recognition. Innovative technology, such as socially assistive robots, has shown to be a viable method for Autism therapy. This paper presents a novel robot-based music-therapy platform for modeling and improving the social responses and behaviors of children with ASD. Our autonomous social interactive system consists of three modules. Module one provides an autonomous initiative positioning system for the robot, NAO, to properly localize and play the instrument (Xylophone) using the robot’s arms. Module two allows NAO to play customized songs …

Evaluation Of Selected Computer Software For Concussion Recovery And Diagnosis, J.P. Jensen

Honors Theses

Acquired traumatic brain injuries, such as concussions, impact many athletes participating in sports, particularly at the high school, collegiate, and professional levels. The risks posed by concussions – particularly when an athlete suffers repeated injuries – demands that protocols and tools be developed to maximize athlete health and safety. Computer technology can perform critical roles in the analysis and management of concussions. While specialized devices in the areas of imaging and impact sensing, are most associated with concussion management, researchers within the last two decades have increasingly explored the incorporation of various consumer technologies into the identification and treatment of …

Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen

Engineering Faculty Articles and Research

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …

Barriers And Enablers For Older Adults Participating In A Home-Based Pragmatic Exercise Program Delivered And Monitored By Amazon Alexa: A Qualitative Study, Paul Jansons, Jackson Fyfe, Jack Dalla Via, Robin M. Daly, Eugene Gvozdenko, David Scott

Research outputs 2022 to 2026

Background: The remote delivery and monitoring of individually-tailored exercise programs using voice-controlled intelligent personal assistants (VIPAs) that support conversation-based interactions may be an acceptable alternative model of digital health delivery for older adults. The aim of this study was to evaluate the enablers and barriers for older adults participating in a home-based exercise program delivered and monitored by VIPAs. Method: This qualitative study used videoconferencing to conduct semi-structured interviews following a 12-week, prospective single-arm pilot study in 15 adults aged 60 to 89 years living alone in the community. All participants were prescribed an individualized, brief (10 min, 2–4 times …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

Advancing Ubiquitous Collaboration For Telehealth - A Framework To Evaluate Technology-Mediated Collaborative Workflow For Telehealth, Hypertension Exam Workflow Study, Christopher Bondy Ph.D., Linlin Chen Ph.D, Pamela Grover Md, Pengcheng Shi Ph.D

Articles

Healthcare systems are under siege globally regarding technology adoption; the recent pandemic has only magnified the issues. Providers and patients alike look to new enabling technologies to establish real-time connectivity and capability for a growing range of remote telehealth solutions. The migration to new technology is not as seamless as clinicians and patients would like since the new workflows pose new responsibilities and barriers to adoption across the telehealth ecosystem. Technology-mediated workflows (integrated software and personal medical devices) are increasingly important in patient-centered healthcare; software-intense systems will become integral in prescribed treatment plans [1]. My research explored the path to …