Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons^™

Assessing The Reidentification Risks Posed By Deep Learning Algorithms Applied To Ecg Data, Arin Ghazarian, Jianwei Zheng, Daniele Struppa, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

ECG (Electrocardiogram) data analysis is one of the most widely used and important tools in cardiology diagnostics. In recent years the development of advanced deep learning techniques and GPU hardware have made it possible to train neural network models that attain exceptionally high levels of accuracy in complex tasks such as heart disease diagnoses and treatments. We investigate the use of ECGs as biometrics in human identification systems by implementing state-of-the-art deep learning models. We train convolutional neural network models on approximately 81k patients from the US, Germany and China. Currently, this is the largest research project on ECG identification. …

The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang

Medical Student Research Symposium

Background: Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.

Methods: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM_2.5), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases …

Development Of Optical Nanomaterial For Enhanced Cerenkov Imaging, Qize Zhang

Dissertations, Theses, and Capstone Projects

Cancer is a significant public health problem worldwide and is the second leading cause of death in the United States. Imaging has increasingly been used over the last two decades to improve the diagnosis and guidance of tumor tissue removal surgery. Among the most widely used techniques for in vivo imaging are planar and tomographic fluorescence imaging and bioluminescence imaging. Despite their utility, these techniques are primarily restricted to preclinical use. Factors that have prevented translation from the bench to the bedside include depth-penetration considerations, regulatory issues, and toxicity. A recent development in nuclear imaging has been the ability to …

Markerless Tumor Tracking Using Kalman Filter And Deep Learning, Anisha Kapoor, Mark Albert

Computer Science Research Seminars and Symposia

According to the Center for Disease Control, more people die from lung cancer than any other cancer in the United States. A complication that arises from lung cancer treatment, radiation therapy, is radiation pneumonitis. Radiation pneumonitis can be fatal and affects over 23% of patients.

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

Electronic Theses and Dissertations

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

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 …

“Translation Of Hdac6 Pet Imaging Using [18f]Ekz-001 – Cgmp Production And Measurement Of Hdac6 Target Occupancy In Nhps” – A Review, Dania Rahal

Chemistry & Biochemistry Undergraduate Honors Theses

The inhibition of histone deacetylase 6 (HDAC6) has been reported to alleviate the effects of neurodegenerative diseases such as Alzheimer’s disease. The brain-penetrant PET radioligand [¹⁸F]EKZ-001 has high affinity and selectivity towards HDAC6 and therefore suggests great promise in therapeutic treatment studies and development for neurodegenerative diseases. “Translation of HDAC6 PET imaging using [¹⁸F]EKZ-001 – cGMP production and measurement of HDAC6 target occupancy in NHPs” has achieved an effective, fully automated method of producing [¹⁸F]EKZ-001 in compliance with current good manufacturing practices (cGMP) to support the translation of [¹⁸F]EKZ-001 PET for …

Estimating The Analytical Performance Of Raman Spectroscopy For Quantification Of Active Ingredients In Human Stratum Corneum, Hichem Kichou, Emilie Munnier, Yuri Dancik, Kamilia Kemel, Hugh Byrne, Ali Tfayli, Dominique Bertrand, Martin Soucé, Igor Chourpa, Franck Bonnier

Articles

Confocal Raman microscopy (CRM) has become a versatile technique that can be applied routinely to monitor skin penetration of active molecules. In the present study, CRM coupled to multivariate analysis (namely PLSR—partial least squares regression) is used for the quantitative measurement of an active ingredient (AI) applied to isolated (ex vivo) human stratum corneum (SC), using systematically varied doses of resorcinol, as model compound, and the performance is quantified according to key figures of merit defined by regulatory bodies (ICH, FDA, and EMA). A methodology is thus demonstrated to establish the limit of detection (LOD), precision, accuracy, sensitivity (SEN), and …

Using Machine Learning To Recognize Chronic Rhinosinusitis, Irene Liu '23

Student Publications & Research

Chronic Rhinosinusitis (CRS) is a nasal disease characterized by the inflammation of the mucosa and paranasal sinuses with a duration of at least 12 consecutive weeks. So, to diagnose CRS, one needs to keep a record of their symptoms for ~12 weeks before they are recommended to get a tomography which will allow physicians to classify them as a patient with CRS or without. This is a timely and costly process; thus, machine learning should be used to speed the process up. Since patients with CRS have more obstructed noses, the sound produced should be different than an individual without …

Three-Heartbeat Multilead Ecg Recognition Method For Arrhythmia Classification, Liang-Hung Wang, Yan-Ting Yu, Wei Liu, Lu Xu, Chao-Xin Xie, Tao Yang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification …

Succinic Semialdehyde Dehydrogenase Deficiency (Ssadhd): Qualitative Needs Assessment For Patients With A Rare Neurological Disorder, Cassandra Bovee, Kayla Woodring

Annual Research Symposium

No abstract provided.

A High Precision Machine Learning-Enabled System For Predicting Idiopathic Ventricular Arrhythmia Origins, Jianwei Zheng, Guohua Fu, Daniele Struppa, Islam Abudayyeh, Tahmeed Contractor, Kyle Anderson, Huimin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Background: Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy.

Method: A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, …

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

School of Public Health Faculty Publications

No abstract provided.

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 …

Peptidomics Analysis Reveals Changes In Small Urinary Peptides In Patients With Interstitial Cystitis/Bladder Pain Syndrome, Md Shadman Ridwan Abid, Haowen Qiu, Bridget Tripp, Aline De Lima Leite, Heidi E. Roth, Jiri Adamec, Robert Powers, James W. Checco

Department of Chemistry: Faculty Publications

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic and debilitating pain disorder of the bladder and urinary tract with poorly understood etiology. A definitive diagnosis of IC/BPS can be challenging because many symptoms are shared with other urological disorders. An analysis of urine presents an attractive and non-invasive resource for monitoring and diagnosing IC/BPS. The antiproliferative factor (APF) peptide has been previously identified in the urine of IC/BPS patients and is a proposed biomarker for the disorder. Nevertheless, other small urinary peptides have remained uninvestigated in IC/BPS primarily because protein biomarker discovery efforts employ protocols that remove small endogenous peptides. …

Why Do Family Members Reject Ai In Health Care? Competing Effects Of Emotions, Eun Hee Park, Karl Werder, Lan Cao, Balasubramaniam Ramesh

Information Technology & Decision Sciences Faculty Publications

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find …

A Review Of Monte Carlo Methods And Their Application In Medical Physics For Simulating Radiation Transport, Joe Shields

Honors Theses and Capstones

Monte Carlo methods are used to calculate statistical behavior through the use of random number generators and probability density functions. They have been used extensively in medical physics for research in radiotherapy, designing technology, dosimetry, and advanced clinical applications. This paper provides a background on Monte Carlo methods and a review of radiation therapy physics and dosimetry. Additionally, there is a discussion of the different ways Monte Carlo methods are used in medical physics as well as a review of current research related to Monte Carlo methods. The final portion of this paper contains my own Monte Carlo simulation using …

Robust Testing Of Paired Outcomes Incorporating Covariate Effects In Clustered Data With Informative Cluster Size, Sandipan Dutta

Mathematics & Statistics Faculty Publications

Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a cluster size) is correlated to the paired outcomes or the paired differences. There have been some attempts to develop robust rank-based tests for comparing paired outcomes in such complex clustered data. Most of these existing rank tests developed for paired outcomes in clustered data compare the marginal distributions in a pair and ignore any covariate effect on …

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

Datasets

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

Construction Of A Repeatable Framework For Prostate Cancer Lesion Binary Semantic Segmentation Using Convolutional Neural Networks, Ian Vincent O. Mirasol, Patricia Angela R. Abu, Rosula Sj Reyes

Department of Information Systems & Computer Science Faculty Publications

Prostate cancer is the 3rd most diagnosed cancer overall. Current screening methods such as the prostate-specific antigen test could result in overdiagonosis and overtreatment while other methods such as a transrectal ultrasonography are invasive. Recent medical advancements have allowed the use of multiparametric MRI — a noninvasive and reliable screening process for prostate cancer. However, assessment would still vary from different professionals introducing subjectivity. While con-volutional neural network has been used in multiple studies to ob-jectively segment prostate lesions, due to the sensitivity of datasets and varying ground-truth established used in these studies, it is not possible to reproduce and …

Refinement Of Alphafold2 Models Against Experimental And Hybrid Cryo-Em Density Maps, Maytha Alshammari, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Recent breakthroughs in deep learning-based protein structure prediction show that it is possible to obtain highly accurate models for a wide range of difficult protein targets for which only the amino acid sequence is known. The availability of accurately predicted models from sequences can potentially revolutionise many modelling approaches in structural biology, including the interpretation of cryo-EM density maps. Although atomic structures can be readily solved from cryo-EM maps of better than 4 Å resolution, it is still challenging to determine accurate models from lower-resolution density maps. Here, we report on the benefits of models predicted by AlphaFold2 (the best-performing …

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

Études primaires

Rationale

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

Method

A new …

Deep Convolutional Neural Networks For Accurate Diagnosis Of Covid-19 Patients Using Chest X-Ray Image Databases From Italy, Canada, And The Usa, Amgad A. Salama, Samy H. Darwish, Samir M. Abdel-Mageed, Radwa A. Meshref, Ehab I. Mohamed

The University of Louisville Journal of Respiratory Infections

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), famously known as COVID-19, has quickly become a global pandemic. Chest X-ray (CXR) imaging has proven reliable, fast, and cost-effective for identifying COVID-19 infections, which proceeds to display atypical unilateral patchy infiltration in the lungs like typical pneumonia. We employed the deep convolutional neural network (DCNN) ResNet-34 to detect and classify CXR images from patients with COVID-19 and Viral Pneumonia and Normal Controls.

Methods: We created a single database containing 781 source CXR images from four different international sub-databases: the Società Italiana di Radiologia Medica e Interventistica (SIRM), the GitHub Database, the …

Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho

SMU Data Science Review

Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …

Machine Learning Approach To Distinguish Ulcerative Colitis And Crohn’S Disease Using Smote (Synthetic Minority Oversampling Technique) Methods, Kris Ghimire, Walter Lai, Yasser Omar, Thad Schwebke, Jamie Vo

SMU Data Science Review

Irritable Bowel Disease (IBD) affects a sizable portion of the US population, causing symptoms such as vomiting, abdominal pain, and diarrhea. Despite the disease’s prevalence, the precise cause is not fully understood. This study consists of endoscopic and histological data from patients diagnosed with IBD and a control population for reference. The machine learning models' focus is to classify patients into IBD types. Several models were analyzed, including decision trees, logistic regression, and k-nearest neighbors. In addition, various methods of SMOTE were applied to determine the most effective transformation and ensuring that the dataset is balanced. The best model with …

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

All Theses

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

Automated Classification Model With Otsu And Cnn Method For Premature Ventricular Contraction Detection, Liang-Hung Wang, Lin-Juan Ding, Chao-Xin Xie, Su-Ya Jiang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Pao-Cheng Huang, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm …

Detection Of Dental Apical Lesions Using Cnns On Periapical Radiograph, Chun-Wei Li, Szu-Yin Lin, He-Sheng Chou, Tsung-Yi Chen, Yu-An Chen, Sheng-Yu Liu, Yu-Lin Liu, Chiung-An Chen, Yen-Cheng Huang, Shih-Lun Chen, Yi-Cheng Mao, Patricia Angela R. Abu, Wei-Yuan Chiang, Wen-Shen Lo

Department of Information Systems & Computer Science Faculty Publications

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or …

Cyclic Peptide-Gadolinium Nanocomplexes As Sirna Delivery Tools, Amir Nasrolahi Shirazi, Muhammad Imran Sajid, Dindyal Mandal, David Stickley, Stephanie Nagasawa, Joshua Long, Sandeep Lohan, Keykavous Parang, Rakesh Kumar Tiwari

Pharmacy Faculty Articles and Research

We have recently reported that a cyclic peptide containing five tryptophan, five arginine, and one cysteine amino acids [(WR)₅C], was able to produce peptide-capped gadolinium nanoparticles, [(WR)₅C]-GdNPs, in the range of 240 to 260 nm upon mixing with an aqueous solution of GdCl₃. Herein, we report [(WR)₅C]-GdNPs as an efficient siRNA delivery system. The peptide-based gadolinium nanoparticles (50 µM) did not exhibit significant cytotoxicity (~93% cell viability at 50 µM) in human leukemia T lymphoblast cells (CCRF-CEM) and triple-negative breast cancer cells (MDA-MB-231) after 48 h. Fluorescence-activated cell sorting (FACS) analysis indicated …

Advancing Proper Dataset Partitioning And Classification Of Visual Search And The Vigilance Decrement Using Eeg Deep Learning Algorithms, Alexander J. Kamrud

Theses and Dissertations

Electroencephalography (EEG) classification of visual search and vigilance tasks has vast potential in its benefits. In future human-machine teaming systems, EEG could act as the tool for operator state assessment, enabling AI teammates to know when to assist the operator in these tasks, with the potential to lead to increased safety of operations, better training systems for our operators, and improved operational effectiveness. This research investigates deep learning methods which utilize EEG signals to classify the efficiency of an operator's search and to classify whether an operator is in a decrement during a vigilance type task, and investigates performing these …