Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Predicting The Need For Cardiovascular Surgery: A Comparative Study Of Machine Learning Models, Arman Ghavidel, Pilar Pazos, Rolando Del Aguila Suarez, Alireza Atashi

Engineering Management & Systems Engineering Faculty Publications

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques …

Selecting And Evaluating Key Mds-Updrs Activities Using Wearable Devices For Parkinson's Disease Self-Assessment, Yuting Zhao, Xulong Wang, Xiyang Peng, Ziheng Li, Fengtao Nan, Menghui Zhuo, Jun Qi, Yun Yang, Zhong Zhao, Lida Xu, Po Yang

Information Technology & Decision Sciences Faculty Publications

Parkinson's disease (PD) is a complex neurodegenerative disease in the elderly. This disease has no cure, but assessing these motor symptoms will help slow down that progression. Inertial sensing-based wearable devices (ISWDs) such as mobile phones and smartwatches have been widely employed to analyse the condition of PD patients. However, most studies purely focused on a single activity or symptom, which may ignore the correlation between activities and complementary characteristics. In this paper, a novel technical pipeline is proposed for fine-grained classification of PD severity grades, which identify the most representative activities. We also propose a multi-activities combination scheme based …

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, …

Image-To-Mesh Conversion Method For Multi-Tissue Medical Image Computing Simulations, Fotis Drakopoulos, Yixun Liu, Kevin Garner, Nikos Chrisochoides

Computer Science Faculty Publications

Converting a three-dimensional medical image into a 3D mesh that satisfies both the quality and fidelity constraints of predictive simulations and image-guided surgical procedures remains a critical problem. Presented is an image-to-mesh conversion method called CBC3D. It first discretizes a segmented image by generating an adaptive Body-Centered Cubic mesh of high-quality elements. Next, the tetrahedral mesh is converted into a mixed element mesh of tetrahedra, pentahedra, and hexahedra to decrease element count while maintaining quality. Finally, the mesh surfaces are deformed to their corresponding physical image boundaries, improving the mesh’s fidelity. The deformation scheme builds upon the ITK open-source library …

Enhancing Heart Disease Prediction With Reinforcement Learning And Data Augmentation, Gayathri R., Sangeetha S. K. B., Sandeep Kumar Mathivanan, Hariharan Rajadurai, Benjula Anbu Malar Mb, Saurav Mallik, Hong Qin

Computer Science Faculty Publications

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning's skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation …

Enhanced Skin Cancer Diagnosis Through Grid Search Algorithm-Optimized Deep Learning Models For Skin Lesion Analysis, Rudresh Pillai, Neha Sharma, Sheifali Gupta, Deepali Gupta, Sapna Juneja, Saurav Malik, Hong Qin, Mohammed S. Alqahtani, Amel Ksibi

Computer Science Faculty Publications

Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)-CNN1, CNN2, and CNN3-each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal …

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …

Machine-Learning-Enabled Diagnostics With Improved Visualization Of Disease Lesions In Chest X-Ray Images, Md. Fashiar Rahman, Tzu-Liang (Bill) Tseng, Michael Pokojovy, Peter Mccaffrey, Eric Walser, Scott Moen, Alex Vo, Johnny C. Ho

Mathematics & Statistics Faculty Publications

The class activation map (CAM) represents the neural-network-derived region of interest, which can help clarify the mechanism of the convolutional neural network’s determination of any class of interest. In medical imaging, it can help medical practitioners diagnose diseases like COVID-19 or pneumonia by highlighting the suspicious regions in Computational Tomography (CT) or chest X-ray (CXR) film. Many contemporary deep learning techniques only focus on COVID-19 classification tasks using CXRs, while few attempt to make it explainable with a saliency map. To fill this research gap, we first propose a VGG-16-architecture-based deep learning approach in combination with image enhancement, segmentation-based region …

Cumulative Distribution Function And Spatially Resolved Surface-Enhanced Raman Spectroscopy For The Quantitative Analysis Of Emtricitabine, Jana Hrncirova, Marguerite R. Butler, Sucharita Dutta, Meredith R. Clark, John B. Cooper

Chemistry & Biochemistry Faculty Publications

Surface-enhanced Raman spectroscopy (SERS) has exceptional analytical sensitivity and selectivity. However, SERS irreproducibility presents an obstacle when using it for precise quantitative measurements. In this study, colloidal nanoparticles evaporated to dryness are used as a SERS active surface for the detection of the HIV drug emtricitabine (FTC; trade name Emtriva). Despite the irreproducibility of the SERS resulting from the stochastic process of evaporation, using a SERS scanning instrument, the SERS enhancement factors of spatially resolved spectra have a well-defined distribution of signals for a given analyte concentration. This distribution follows a power law function ranging from weak (very abundant signals) …

Development Of Functional Plasmonic Nanoceria For The Ultrasensitive Detection Of Ebola Glycoprotein, Carissa A. Sutton

Graduate Theses/Dissertations

Infectious diseases have been increasing in both severity and prevalence, leading to increasing economic burdens and health concerns. Ebola hemorrhagic fever is an infectious disease that is both highly communicable and highly fatal. Current detection methods for the virus have high specificity and sensitivity but have long and/or complicated procedures that do not allow for early virus detection in highly affected areas. Compared to the lengthy procedure of reverse transcription-polymerase chain reaction (RT-PCR), real-time RT-PCR is more time-efficient; however, specificity and sensitivity are reduced. Since early detection of the virus is the only way to increase survival rates, sensitive detection …

Investigation Of The Effect Of Preparation Parameters On The Structural And Mechanical Properties Of Gelatin/Elastin/Sodium Hyaluronate Scaffolds Fabricated By The Combined Foaming And Freeze-Drying Techniques, Mansour Qamash, S. Misagh Imani, Meisam Omidi, Ciara Glancy, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

This paper aimed to evaluate the effects of different preparation parameters, including agitation speed, agitation time, and chilling temperature, on the structural and mechanical properties of a novel gelatin/elastin/sodium hyaluronate tissue engineering scaffold, recently developed by our research group. Fabricated using a combination of foaming and freeze-drying techniques, the scaffolds were assessed to understand how these parameters influence their morphology, internal microstructure, porosity, mechanical properties, and degradation behavior. The fabrication process used in this study involved preparing a homogeneous aqueous solution containing 8% gelatin, 2% elastin, and 0.5% sodium hyaluronate (w/v), which was then subjected to mechanical agitation at speeds …

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 …

Detection Of Micrornas In Blood Samples Using Biosensors For The Detection Of Early-Stage Lung Cancer, Nicholas Bruno

Honors Undergraduate Theses

Lung cancer kills more people in the U.S. a year than any other cancer. This is largely because patients often remain asymptomatic until stage 3, by which time the tumor is usually highly malignant. High radiation-producing screenings are the current method of lung tumor detection. These typically involve machinery for tumor visualization, such as computed tomography (CT) scans, positron emission tomography (PET scan), magnetic resonance imaging (MRI), and X-rays. Not only does radiation exposure from this equipment pose a risk to the patient, but any tumors detected by these scanning techniques are likely to be locally advanced. This suggests that …

Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim

Electrical & Computer Engineering Faculty Publications

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …

Ai And Ml-Based Risk Assessment Of Chemicals: Predicting Carcinogenic Risk From Chemical-Induced Genomic Instability, Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, Prachi Pradeep, Madleen Busse, Andreas Luch, Akihiko Hirose, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, …

Advancing Chronic Kidney Disease Prediction Through Machine Learning And Deep Learning With Feature Analysis, Shiddarth Dey Tusar, S. M. Ahad Ali Chowdhury, Md. Jalal Uddin Chowdhury, Rana M. Pir, H. M. Nur A. Alam, Muhammad Rezaur Rahman, Md. Nural Absar Siddiky, Muhammad Enayetur Rahman

Electrical & Computer Engineering Faculty Publications

Chronic Kidney Diesease (CKD) is a significant health issue, ranking as the fourth leading cause of mortality worldwide. The traditional diagnosis and treatment process, reliant on medical experts, is time-consuming. Therefore, thereis an urgent need for more efficient diagnostic methods to improve patient outcomes and reduce mortality rates. In this study, we employ Machine Learning (ML) and Deep Learning (DL) techniques to predict CKD based on important features. Feature analysis was performed using a correlation matrix and the LASSO algo-rithm to identify the most relevant features for model training. We evaluated several ML and DL classifiers, including Logistic Regression (LR), …

Efficient Classification Of Very High Resolution Images, Mohammad I. Nouyed

Graduate Theses, Dissertations, and Problem Reports (ETD)

In recent decades, deep learning approaches have shown significant improvement in various image understanding tasks. However, analysis of high-resolution images remains a major challenge. In this work, we address the challenge of very high-resolution histopathological image (VHRHI) classification using a new information-theoretic discriminative patch selection approach. We show results on a high-resolution image dataset, namely, gigapixel whole slide tissue images for cancer tumors. Then we address how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. These ``weak labels'' are applied throughout the image but describe tumor regions of variable sizes and …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Raman Spectroscopic Analysis Of Human Serum Samples Of Convalescing Covid-19 Positive Patients, Hugh Byrne, Naomi Jackson, Jaythoon Hassan

Articles

Rapid screening, detection and monitoring of viral infection is of critical importance, as exemplified by the rapid spread of SARS-CoV-2, leading to the worldwide pandemic of COVID-19. This is equally the case for the stages of patient convalescence as for the initial stages of infection, to understand the medium and long terms effects, as well as the efficacy of therapeutic interventions. Optical spectroscopic techniques potentially offer an alternative to currently employed techniques of screening for the presence, or the response to infection. In this study, the ability of Raman spectroscopy to distinguish between samples of the serum of convalescent COVID-19 …

Applications Of Causal Inference Methods For The Estimation Of Effects Of Bone Marrow Transplant And Prescription Drugs On Survival Of Aplastic Anemia Patients, Yesha M. Patel

Computational and Data Sciences (PhD) Dissertations

This dissertation provides an in-depth exploration into the treatment effectiveness for aplastic anemia using causal inference methods, structured around three pivotal research papers. Each paper contributes to a nuanced understanding of treatment impacts, specifically focusing on bone marrow transplantation (BMT) and prescription drugs, and the identification of optimal treatment strategies.

The first paper, "Causal Inference Analysis for Assessing the Effect of Bone Marrow Transplantation on the One-Year Survival of Adult and Pediatric Aplastic Anemia Patients," sets the foundation. It examines the short-term effectiveness of BMT in both adult and pediatric patients, providing crucial insights into how this treatment affects survival …

Development Of A Physics-Informed Neural Network For Prediction Of Blood Flow, Marcello Vittorio Mattei Di Eugenio

Theses and Dissertations

Abstract—Objective: We propose a new neural network architecture that accepts point clouds and outputs 3D velocity profiles for aneurysm geometries. Methods: We generated a synthetic aneurysm 3D flow dataset using CFD and used it to train our model architecture and compare it with other popular architectures like U-net and PointNet. We incorporate tools for improving model performance such as incorporating a distance function, a physics-informed loss to enforce the law of mass conservation, and the Huber loss to learn patterns across heterogeneous velocity components of multiple dimensions. Results: The tools implemented together with our architecture achieved the best performance on …

Editorial For "Automated Breast Density Assessment In Mri Using Deep Learning And Radiomics: Strategies For Reducing Inter-Observer Variability"., Pegah Khosravi

Publications and Research

No abstract provided.

Raman Spectral Cytopathology For Cancer Diagnostic Applications, Fiona Lyng, Damian Traynor, Isha Behl, Declan O'Dea, Hugh Byrne

Books/Book Chapters

Histopathology is currently regarded as the “gold standard” patient sampling technique for cancer diagnostics. It involves extraction of a tissue sample, or biopsy, which is then sectioned and stained for evaluation by a pathologist [1]. Cytopathology, on the other hand, is the field of disease diagnosis at the cellular level [2], necessitating only the microscopic evaluation of samples of cells which can be harvested by less invasive methods such as exfoliative brushing, needle probes or from biofluids. Based on the identification of abnormalities in cellular appearance, cytopathology is currently widely used to aid in the screening of cancer, as well …

Thermal Dose Inactivation Of Escherichia Coli By Magnetic Induced Hyperthermia, Silverio A. Lopez, Carlos Trevino De Leo, Ivan Davila, Karen S. Martirosyan

Research Symposium

Background: Apoptosis of mutated cells via magnetic hyperthermia has gained advocacy as technology capable of being used in lieu of chemotherapy for targeting cancer tumors. Progress of nanotechnology offers effective remote heating of magnetic fluid via hyperthermia. The heating and specific power absorption of these nanoparticles use in the magnetic fluid are dependent on particle properties and treatment locations.

Methods: Nanoparticles were fabricated using microfluidic system by interaction of two solutions containing 2Fe(NO3)3+FeSO4 and NaOH+2%Dextran to create nanostructured media with a biocompatible dextran coating and a Fe3O4 core. The nanoparticles, of a concentration of 5mg/ml, were placed in a vile …

Additive Effects Of Cyclic Peptide [R4w4] When Added Alongside Azithromycin And Rifampicin Against Mycobacterium Avium Infection, Melissa Kelley, Kayvan Sasaninia, Arbi Abnousian, Ali Badaoui, James Owens, Abrianna Beever, Nala Kachour, Rakesh Kumar Tiwari, Vishwanath Venketaraman

Pharmacy Faculty Articles and Research

Mycobacterium avium (M. avium), a type of nontuberculous mycobacteria (NTM), poses a risk for pulmonary infections and disseminated infections in immunocompromised individuals. Conventional treatment consists of a 12-month regimen of the first-line antibiotics rifampicin and azithromycin. However, the treatment duration and low antibiotic tolerability present challenges in the treatment of M. avium infection. Furthermore, the emergence of multidrug-resistant mycobacterium strains prompts a need for novel treatments against M. avium infection. This study aims to test the efficacy of a novel antimicrobial peptide, cyclic [R4W4], alongside the first-line antibiotics azithromycin and rifampicin in reducing M. avium survival. Colony-forming unit (CFU) …

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 …

Algorithmic Bias: Causes And Effects On Marginalized Communities, Katrina M. Baha

Undergraduate Honors Theses

Individuals from marginalized backgrounds face different healthcare outcomes due to algorithmic bias in the technological healthcare industry. Algorithmic biases, which are the biases that arise from the set of steps used to solve or analyze a problem, are evident when people from marginalized communities use healthcare technology. For example, many pulse oximeters, which are the medical devices used to measure oxygen saturation in the blood, are not able to accurately read people who have darker skin tones. Thus, people with darker skin tones are not able to receive proper health care due to their pulse oximetry data being inaccurate. This …

Analytical Approach For Monitoring The Behavior Of Patients With Pancreatic Adenocarcinoma At Different Stages As A Function Of Time, Aditya Chakaborty Dr, Chris P. Tsokos Dr

Biology and Medicine Through Mathematics Conference

No abstract provided.

Artificial Intelligence In Neuroradiology: A Scoping Review Of Some Ethical Challenges, Pegah Khosravi, Mark Schweitzer

Publications and Research

Artificial intelligence (AI) has great potential to increase accuracy and efficiency in many aspects of neuroradiology. It provides substantial opportunities for insights into brain pathophysiology, developing models to determine treatment decisions, and improving current prognostication as well as diagnostic algorithms. Concurrently, the autonomous use of AI models introduces ethical challenges regarding the scope of informed consent, risks associated with data privacy and protection, potential database biases, as well as responsibility and liability that might potentially arise. In this manuscript, we will first provide a brief overview of AI methods used in neuroradiology and segue into key methodological and ethical challenges. …

Risk Assessment Framework For Evaluation Of Cybersecurity Threats And Vulnerabilities In Medical Devices, Maureen S. Van Devender

Graduate Theses and Dissertations (2019 - present)

Medical devices are vulnerable to cybersecurity exploitation and, while they can provide improvements to clinical care, they can put healthcare organizations and their patients at risk of adverse impacts. Evidence has shown that the proliferation of devices on medical networks present cybersecurity challenges for healthcare organizations due to their lack of built-in cybersecurity controls and the inability for organizations to implement security controls on them. The negative impacts of cybersecurity exploitation in healthcare can include the loss of patient confidentiality, risk to patient safety, negative financial consequences for the organization, and loss of business reputation. Assessing the risk of vulnerabilities …