Digital Commons Network^™

Radiomic Biomarkers Of Locoregional Recurrence: Prognostic Insights From Oral Cavity Squamous Cell Carcinoma Preoperative Ct Scans, Xiao Ling, Gregory S. Alexander, Jason Molitoris, Jinhyuk Choi, Lisa Schumaker, Phuoc Tran, Ranee Mehra, Daria Gaykalova, Lei Ren

Department of Radiation Oncology Faculty Papers

INTRODUCTION: This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients.

METHODS: Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The …

Cardiogpt: An Ecg Interpretation Generation Model, Guohua Fu, Jianwei Zheng, Islam Abudayyeh, Chizobam Ani, Cyril Rakovski, Louis Ehwerhemuepha, Hongxia Lu, Yongjuan Guo, Shenglin Liu, Huimin Chu, Bing Yang

Mathematics, Physics, and Computer Science Faculty Articles and Research

Numerous supervised learning models aimed at classifying 12-lead electrocardiograms into different groups have shown impressive performance by utilizing deep learning algorithms. However, few studies are dedicated to applying the Generative Pre-trained Transformer (GPT) model in interpreting electrocardiogram (ECG) using natural language. Thus, we are pioneering the exploration of this uncharted territory by employing the CardioGPT model to tackle this challenge. We used a dataset of ECGs (standard 10s, 12-channel format) from adult patients, with 60 distinct rhythms or conduction abnormalities annotated by board-certified, actively practicing cardiologists. The ECGs were collected from The First Affiliated Hospital of Ningbo University and Shanghai …

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

Physical Therapy Faculty Articles and Research

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

Deep Learning-Based Multimodality Classification Of Chronic Mild Traumatic Brain Injury Using Resting-State Functional Mri And Pet Imaging, Faezeh Vedaei, Najmeh Mashhadi, Mahdi Alizadeh, George Zabrecky, Daniel A. Monti, Md, Nancy Wintering, Emily Navarreto, Chloe Hriso, Andrew B. Newberg, Feroze B. Mohamed

Department of Radiology Faculty Papers

Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI (n = 83) and healthy controls (HCs) (n = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and …

Efficient Thorax Disease Classification And Localization Using Dcnn And Chest X-Ray Images, Zeeshan Ahmad, Ahmad Kamran Malik, Nafees Qamar, Saif Ul Islam

Psychology Department Faculty Journal Articles

Thorax disease is a life-threatening disease caused by bacterial infections that occur in the lungs. It could be deadly if not treated at the right time, so early diagnosis of thoracic diseases is vital. The suggested study can assist radiologists in more swiftly diagnosing thorax disorders and in the rapid airport screening of patients with a thorax disease, such as pneumonia. This paper focuses on automatically detecting and localizing thorax disease using chest X-ray images. It provides accurate detection and localization using DenseNet-121 which is foundation of our proposed framework, called Z-Net. The proposed framework utilizes the weighted cross-entropy loss …

A Novel Machine-Learning Framework Based On A Hierarchy Of Dispute Models For The Identification Of Fish Species Using Multi-Mode Spectroscopy, Mitchell Sueker, Amirreza Daghighi, Alireza Akhbardeh, Nicholas Mackinnon, Gregory Bearman, Insuck Baek, Chansong Hwang, Jianwei Qin, Amanda M Tabb, Jiahleen B Roungchun, Rosalee S Hellberg, Fartash Vasefi, Moon Kim, Kouhyar Tavakolian, Hossein Kashani Zadeh

Journal Articles

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of …

Extracellular Vesicles In Triple–Negative Breast Cancer: Immune Regulation, Biomarkers, And Immunotherapeutic Potential, Kaushik Das, Subhojit Paul, Arnab Ghosh, Saurabh Gupta, Tanmoy Mukherjee, Prem Shankar, Anshul Sharma, Shiva Keshava, Subhash C. Chauhan, Vivek Kumar Kashyap

School of Medicine Publications and Presentations

Triple–negative breast cancer (TNBC) is an aggressive subtype accounting for ~10–20% of all human BC and is characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) amplification. Owing to its unique molecular profile and limited targeted therapies, TNBC treatment poses significant challenges. Unlike other BC subtypes, TNBC lacks specific molecular targets, rendering endocrine therapies and HER2–targeted treatments ineffective. The chemotherapeutic regimen is the predominant systemic treatment modality for TNBC in current clinical practice. However, the efficacy of chemotherapy in TNBC is variable, with response rates varying between a wide range …

Pca-Clf: A Classifier Of Prostate Cancer Patients Into Patients With Indolent And Aggressive Tumors Using Machine Learning, Yashwanth Karthik Kumar Mamidi, Tarun Karthik Kumar Mamidi, Md Wasi Ul Kabir, Jiande Wu, Md Tamjidul Hoque, Chindo Hicks

School of Medicine Faculty Publications

A critical unmet medical need in prostate cancer (PCa) clinical management centers around distinguishing indolent from aggressive tumors. Traditionally, Gleason grading has been utilized for this purpose. However, tumor classification using Gleason Grade 7 is often ambiguous, as the clinical behavior of these tumors follows a variable clinical course. This study aimed to investigate the application of machine learning techniques (ML) to classify patients into indolent and aggressive PCas. We used gene expression data from The Cancer Genome Atlas and compared gene expression levels between indolent and aggressive tumors to identify features for developing and validating a range of ML …

Prognostic Evaluation Of Re-Resection For Recurrent Glioblastoma Using The Novel Rano Classification For Extent Of Resection: A Report Of The Rano Resect Group, Philipp Karschnia, Antonio Dono, Jacob S Young, Stephanie T Juenger, Nico Teske, Levin Häni, Tommaso Sciortino, Christine Y Mau, Francesco Bruno, Luis Nunez, Ramin A Morshed, Alexander F Haddad, Michael Weller, Martin Van Den Bent, Juergen Beck, Shawn Hervey-Jumper, Annette M Molinaro, Nitin Tandon, Roberta Rudà, Michael A Vogelbaum, Lorenzo Bello, Oliver Schnell, Stefan J Grau, Susan M Chang, Mitchel S Berger, Yoshua Esquenazi, Joerg-Christian Tonn

Journal Articles

BACKGROUND: The value of re-resection in recurrent glioblastoma remains controversial as a randomized trial that specifies intentional incomplete resection cannot be justified ethically. Here, we aimed to (1) explore the prognostic role of extent of re-resection using the previously proposed Response Assessment in Neuro-Oncology (RANO) classification (based upon residual contrast-enhancing (CE) and non-CE tumor), and to (2) define factors consolidating the surgical effects on outcome.

METHODS: The RANO resect group retrospectively compiled an 8-center cohort of patients with first recurrence from previously resected glioblastomas. The associations of re-resection and other clinical factors with outcome were analyzed. Propensity score-matched analyses were …

Prognostic Validation Of A New Classification System For Extent Of Resection In Glioblastoma: A Report Of The Rano Resect Group, Philipp Karschnia, Jacob S Young, Antonio Dono, Levin Häni, Tommaso Sciortino, Francesco Bruno, Stephanie T Juenger, Nico Teske, Ramin A Morshed, Alexander F Haddad, Yalan Zhang, Sophia Stoecklein, Michael Weller, Michael A Vogelbaum, Juergen Beck, Nitin Tandon, Shawn Hervey-Jumper, Annette M Molinaro, Roberta Rudà, Lorenzo Bello, Oliver Schnell, Yoshua Esquenazi, Maximilian I Ruge, Stefan J Grau, Mitchel S Berger, Susan M Chang, Martin Van Den Bent, Joerg-Christian Tonn

Journal Articles

BACKGROUND: Terminology to describe extent of resection in glioblastoma is inconsistent across clinical trials. A surgical classification system was previously proposed based upon residual contrast-enhancing (CE) tumor. We aimed to (1) explore the prognostic utility of the classification system and (2) define how much removed non-CE tumor translates into a survival benefit.

METHODS: The international RANO resect group retrospectively searched previously compiled databases from 7 neuro-oncological centers in the USA and Europe for patients with newly diagnosed glioblastoma per WHO 2021 classification. Clinical and volumetric information from pre- and postoperative MRI were collected.

RESULTS: We collected 1,008 patients with newly …

Deep Learning-Enabled Fully Automated Pipeline System For Segmentation And Classification Of Single-Mass Breast Lesions Using Contrast-Enhanced Mammography: A Prospective, Multicentre Study, Tiantian Zheng, Fan Lin, Xianglin Li, Tongpeng Chu, Jing Gao, Shijie Zhang, Ziyin Li, Yajia Gu, Simin Wang, Feng Zhao, Heng Ma, Haizhu Xie, Cong Xu, Haicheng Zhang, Ning Mao

Journal Articles

Background

Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial intelligence model that diagnosed single-mass breast lesions on contrast-enhanced mammography (CEM) for assisting the diagnostic workflow.

Methods

A total of 1912 women with single-mass breast lesions on CEM images before biopsy or surgery were included from June 2017 to October 2022 at three centres in China. Samples were divided into training and validation sets, internal testing set, pooled external testing set, and prospective testing set. …

Machine-Learning-Based Head Impact Subtyping Based On The Spectral Densities Of The Measurable Head Kinematics, Xianghao Zhan, Yiheng Li, Yuzhe Liu, Nicholas J. Cecchi, Samuel J. Raymond, Zhou Zhou, Hossein Vahid Alizadeh, Jesse Ruan, Saeed Barbat, Stephen Tiernan, Olivier Gevaert, Michael M. Zeineh, Gerald A. Grant, David B. Camarillo

Articles

Traumatic brain injury can be caused by head impacts, but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo and the characteristics of different types of impacts are not well studied. We investigated the spectral characteristics of different head impact types with kinematics classification. Methods: Data was analyzed from 3,262 head impacts from lab reconstruction, American football, mixed martial arts, and publicly available car crash data. A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e.g., football, car crash, …

Common Statistical Concepts In The Supervised Machine Learning Arena, Hooman H Rashidi, Samer Albahra, Scott Robertson, Nam K Tran, Bo Hu

Journal Articles

One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be objectively assessed without appropriate statistical measurements. The scope of statistics within the ML realm is rather broad and cannot be adequately covered in a single review article. Therefore, here we will mainly focus on the common statistical concepts that pertain to supervised ML (i.e. classification and regression) along with …

Prediction Of Rapid Early Progression And Survival Risk With Pre-Radiation Mri In Who Grade 4 Glioma Patients, Walia Farzana, Mustafa M. Basree, Norou Diawara, Zeina Shboul, Sagel Dubey, Marie M. Lockheart, Mohamed Hamza, Joshua D. Palmer, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Rapid early progression (REP) has been defined as increased nodular enhancement at the border of the resection cavity, the appearance of new lesions outside the resection cavity, or increased enhancement of the residual disease after surgery and before radiation. Patients with REP have worse survival compared to patients without REP (non-REP). Therefore, a reliable method for differentiating REP from non-REP is hypothesized to assist in personlized treatment planning. A potential approach is to use the radiomics and fractal texture features extracted from brain tumors to characterize morphological and physiological properties. We propose a random sampling-based ensemble classification model. The proposed …

Efficient Framework For Brain Tumor Detection Using Different Deep Learning Techniques, Fatma Taher, Mohamed R. Shoaib, Heba M. Emara, Khaled M. Abdelwahab, Fathi E. Abd El-Samie, Mohammad T. Haweel

All Works

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is …

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, …

Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi

All Works

COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which …

Prediction Of Electronic Nicotine Delivery Systems Use In Copdgene Using Multi-Omics Biomarkers, Andrew Gregory, Zhonghui Xu, Katherine Pratte, Seth Berman, Noah Lichtblau, Robin Lu, Robert Chase, Jeong Yun, Aabida Saferali, Edwin K. Silverman, Craig P. Hersh, Russell P. Bowler, Adel Boueiz, Peter J. Castaldi

Medical Student Research Symposium

Introduction: Biomarkers may be useful for understanding the toxic effects of vaping. Herein, we identified blood transcriptomic and proteomic biomarkers of vaping, related them to prospective health outcomes, and investigated their ability to accurately distinguish vapers from smokers.

Methods: We grouped 3,892 COPDGene study participants as vapers, current smokers, former smokers, or dual users. We tested for associations with 21,471 blood RNA transcripts and 4,979 plasma proteins. We related the significant biomarkers to 6.5 years of incident health events. To assess the discriminative performance of multi-omics for vaping, we constructed linear discriminant analysis models with cross-validation for RNA …

The Classification Of Scoliosis Braces Developed By Sosort With Srs, Ispo, And Posna And Approved By Esprm., Stefano Negrini, Angelo Gabriele Aulisa, Pavel Cerny, Jean Claude De Mauroy, Jeb Mcaviney, Andrew Mills, Sabrina Donzelli, Theodoros B. Grivas, M Timothy Hresko, Tomasz Kotwicki, Hubert Labelle, Louise Marcotte, Martin Matthews, Joe O'Brien, Eric C. Parent, Nigel Price, Rigo Manuel, Luke Stikeleather, Michael G. Vitale, Man Sang Wong, Grant Wood, James Wynne, Fabio Zaina, Marco Brayda Bruno, Suncica Bulat Würsching, Yilgor Caglar, Patrick Cahill, Eugenio Dema, Patrick Knott, Andrea Lebel, Grigorii Lein, Peter O. Newton, Brian G. Smith

Manuscripts, Articles, Book Chapters and Other Papers

PURPOSE: Studies have shown that bracing is an effective treatment for patients with idiopathic scoliosis. According to the current classification, almost all braces fall in the thoracolumbosacral orthosis (TLSO) category. Consequently, the generalization of scientific results is either impossible or misleading. This study aims to produce a classification of the brace types.

METHODS: Four scientific societies (SOSORT, SRS, ISPO, and POSNA) invited all their members to be part of the study. Six level 1 experts developed the initial classifications. At a consensus meeting with 26 other experts and societies' officials, thematic analysis and general discussion allowed to define the classification …

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 …

Innovations In Cervical Spine Trauma: Developing The Next Generation Upper Cervical Spine Injury Classification System, Brian A Karamian, Hannah Levy, Paul D. Minetos, Michael L. Smith, Alex R. Vaccaro

Rothman Institute Faculty Papers

The upper cervical spine not only consists of intricate bony and ligamentous anatomy affording unique flexibility but also has increased susceptibility to injuries. The upper cervical spine trauma can result in a wide spectrum of injuries that can be managed both operatively and nonoperatively. Several existing classification systems have been proposed to describe injuries of the upper cervical spine, many of which rely on anatomic descriptions of injury location. Prior fracture classifications are limited in scope, characterizing fractures restricted to a single region of the upper cervical spine, and fail to provide insight into injury management. The AO Spine Upper …

Exploring The Concept Of The Digital Educator During Covid-19, Fernando Jimenez, Gracia Sanchez, Jose Palma, Luis Miralles-Pechuán, Juan A. Botia

Articles

T In many machine learning classification problems, datasets are usually of high dimensionality and therefore require efficient and effective methods for identifying the relative importance of their attributes, eliminating the redundant and irrelevant ones. Due to the huge size of the search space of the possible solutions, the attribute subset evaluation feature selection methods are not very suitable, so in these scenarios feature ranking methods are used. Most of the feature ranking methods described in the literature are univariate methods, which do not detect interactions between factors. In this paper, we propose two new multivariate feature ranking methods based on …

Smart Covid-3d-Scnn: A Novel Method To Classify X-Ray Images Of Covid-19, Ahed Abugabah, Atif Mehmood, Ahmad Ali Al Zubi, Louis Sanzogni

All Works

The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. …

Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework …

Law Library Blog (October 2021): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

Gray Matter Volumes Discriminate Cognitively Impaired And Unimpaired People With Hiv, Mikki Schantell, Brittany K. Taylor, Brandon Lew, Jennifer O'Neill, Pamela E. May, Susan Swindells, Tony W. Wilson

Journal Articles: Infectious Diseases

BACKGROUND: Current diagnostic criteria of HIV-associated neurocognitive disorders (HAND) rely on neuropsychological assessments. The aim of this study was to evaluate if gray matter volumes (GMV) can distinguish people with HAND, neurocognitively unimpaired people with HIV (unimpaired PWH), and uninfected controls using linear discriminant analyses.

METHODS: A total of 231 participants, including 110 PWH and 121 uninfected controls, completed a neuropsychological assessment and an MRI protocol. Among PWH, HAND (n = 48) and unimpaired PWH (n = 62) designations were determined using the widely accepted Frascati criteria. We then assessed the extent to which GMV, corrected for intracranial volume, could …

Hydronephrosis Classifications: Has Utd Overtaken Apd And Sfu? A Worldwide Survey., Santiago Vallasciani, Anna Bujons Tur, John Gatti, Marcos Machado, Christopher S. Cooper, Marie Klaire Farrugia, Huixia Zhou, Mohammed El Anbari, Pedro-José Lopez

Manuscripts, Articles, Book Chapters and Other Papers

Objective: To collect baseline information on the ultrasonographic reporting preferences.

Method: A 13-multiple choice questionnaire was designed and distributed worldwide among pediatric urologists, pediatric surgeons, and urologists. The statistical analysis of the survey data consisted of 3 steps: a univariate analysis, a bivariate and a multivariate analysis.

Results: Three hundred eighty participants responded from all the continents. The bivariate analysis showed the significant differences in the geographical area, the years of experience and the volume of cases. Most of the physicians prefer the SFU and APD systems because of familiarity and simplicity (37 and 34%, respectively). Respondents noted that their …

A High-Precision Machine Learning Algorithm To Classify Left And Right Outflow Tract Ventricular Tachycardia, Jianwei Zhang, Guohua Fu, Islam Abudayyeh, Magdi Yacoub, Anthony Chang, William Feaster, Louis Ehwerhemuepha, Hesham El-Askary, Xianfeng Du, Bin He, Mingjun Feng, Yibo Yu, Binhao Wang, Jing Liu, Hai Yao, Hulmin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Introduction: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods: We randomly sampled training, validation, and testing …

Bibliometric Review On Applications Of Disease Detection Using Digital Image Processing Techniques, Jayant Jagtap, Rahil Sharma, Aryan Sinha, Nikhil Panda, Amulya Reddy

Library Philosophy and Practice (e-journal)

Advances around the field of deep learning and cognitive computing have allowed mankind to look and solve the problems of the world in a completely new way. Deep learning has been making huge advancements in the field of healthcare, which most importantly focuses upon disease detection and disease prediction. Techniques such as these have been conceptualized the idea of early detection and economical ways of treating the predicted disease in particular. Still, it has been observed that there seems to be no change in the way diagnosis of a particular disease takes place even in the 21^st generation of …

Impact Of Diabetes On The Gut And Salivary Iga Microbiomes, Eric L Brown, Heather T Essigmann, Kristi L Hoffman, Noah W Palm, Sarah M Gunter, Joel M Sederstrom, Joseph F Petrosino, Goo Jun, David Aguilar, William B Perkison, Craig L Hanis, Herbert L Dupont

Journal Articles

Mucosal surfaces like those present in the lung, gut, and mouth interface with distinct external environments. These mucosal gateways are not only portals of entry for potential pathogens but also homes to microbial communities that impact host health. Secretory immunoglobulin A (SIgA) is the single most abundant acquired immune component secreted onto mucosal surfaces and, via the process of immune exclusion, shapes the architecture of these microbiomes. Not all microorganisms at mucosal surfaces are targeted by SIgA; therefore, a better understanding of the SIgA-coated fraction may identify the microbial constituents that stimulate host immune responses in the context of health …