Medicine and Health Sciences Commons^™

Prediction Of Throwing Distance In The Men’S And Women’S Javelin Final At The 2017 Iaaf World Championships, John Krzyszkowski, Kristof Kipp

Exercise Science Faculty Research and Publications

The purpose of this study was to use regularised regression models to identify the most important biomechanical predictors of throwing distance in elite male (M) and female (F) javelin throwers at the 2017 IAAF world championships. Biomechanical data from 13 male and 12 female javelin throwers who competed at the 2017 IAAF world championships were obtained from an official scientific IAAF report. Regularised regression models were used to investigate the associations between throwing distance and release parameters, whole-body kinematic and joint-level kinematic data. The regularised regression models identified two biomechanical predictors of throwing distances in both M and F javelin …

Quantitative Analysis Of Research On Artificial Intelligence In Retinopathy Of Prematurity, Ranjana Agrawal, Manasi Anup Agrawal, Sucheta Kulkarni, Ketan Kotecha, Rahee Walambe

Library Philosophy and Practice (e-journal)

Retinopathy of Prematurity (ROP) is a disease of the eye and a potential source of blindness in low birth weight preterm infants. It is preventable if diagnosed and treated on time. Artificial Intelligence (AI) has played an important role in developing automated screening systems to assist medical experts. There are many traditional literature review articles available that focus on the scientific content of ROP-AI. The researchers also require a bibliometric analysis to become acquainted with the competing groups and new trends in this field. This paper gives a brief overview of ROP and AI systems for ROP screening with a …

The Human In The Middle: Artificial Intelligence In Health Care Summary Proceedings Symposium Presentation And Reactor Panel Of Experts Thomas Jefferson University December 10, 2019., Janice L. Clarke, Alexandria Skoufalos, Steven Klasko, Md, Mba

College of Population Health Faculty Papers

No abstract provided.

Deep-Learning-Based Multivariate Pattern Analysis (Dmvpa): A Tutorial And A Toolbox, Karl M. Kuntzelman, Jacob M. Williams, Phui Cheng Lim, Ashtok Samal, Prahalada K. Rao, Matthew R. Johnson

Center for Brain, Biology, and Behavior: Faculty and Staff Publications

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on …

The Effectiveness Of Image Augmentation In Deep Learning Networks For Detecting Covid-19: A Geometric Transformation Perspective, Mohamed Elgendi, Muhammad Umer Nasir, Qunfeng Tang, David Smith, John Paul Grenier, Catherine Batte, Bradley Spieler, William Donald Leslie, Carlo Menon, Richard Ribbon Fletcher, Newton Howard, Rabab Ward, William Parker, Savvas Nicolaou

School of Medicine Faculty Publications

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often …

Machine Learning By Ultrasonography For Risk Stratification Of Axillary Breast Lymph Nodes, Joshua Yu, John Eisenbrey, Phd, Aylin Tahmasebi, Md

Phase 1

Introduction: Breast cancer is the most common cancer among women worldwide, and ultrasonography has been an essential tool in the management of breast cancer. In order to improve upon ultrasonography efficacy, establishing a machine learning image analysis model would provide additional prognostic and diagnostic factors in the evaluation, monitoring, and treatment of breast cancer.

Methods: This retrospective study utilizes axillary lymph node ultrasound images of patients at Thomas Jefferson University Hospital who have had axillary lymph node biopsies. Automated machine learning of the images was performed on AutoML Vision; Google LLC which generated custom models for classification. About 80% of …

Enhancing Drug Overdose Mortality Surveillance Through Natural Language Processing And Machine Learning, Patrick J. Ward

Theses and Dissertations--Epidemiology and Biostatistics

Epidemiological surveillance is key to monitoring and assessing the health of populations. Drug overdose surveillance has become an increasingly important part of public health practice as overdose morbidity and mortality has increased due in large part to the opioid crisis. Monitoring drug overdose mortality relies on death certificate data, which has several limitations including timeliness and the coding structure used to identify specific substances that caused death. These limitations stem from the need to analyze the free-text cause-of-death sections of the death certificate that are completed by the medical certifier during death investigation. Other fields, including clinical sciences, have utilized …

Transfer Learning Artificial Intelligence For Automated Detection Of Atrial Fibrillation In Patients Undergoing Evaluation For Suspected Obstructive Sleep Apnoea: A Feasibility Study, Nestor Gahungu, Afsin Shariar, David Playford, Christopher Judkins, Eli Gabbay

Medical Papers and Journal Articles

Background: Individuals with obstructive sleep apnoea (OSA) experience a higher burden of atrial fibrillation (AF) than the general population, and many cases of AF remain undetected. We tested the feasibility of an artificial intelligence (AI) approach to opportunistic detection of AF from single-lead electrocardiograms (ECGs) which are routinely recorded during in-laboratory polysomnographic sleep studies.

Methods: Using transfer learning, an existing ECG AI model was applied to 1839 single-lead ECG traces recorded during in-laboratory sleep studies without any training of the algorithm. Manual review of all traces was performed by two trained clinicians who were blinded to each other's review. Discrepancies …

Advancing Cyanobacteria Biomass Estimation From Hyperspectral Observations: Demonstrations With Hico And Prisma Imagery, Ryan E. O'Shea, Nima Pahlevan, Brandon Smith, Mariano Bresciani, Todd Egerton, Claudia Giardino, Lin Li, Tim Moore, Antonio Ruiz-Verdu, Steve Ruberg, Stefan G.H. Simis, Richard Stumpf, Diana Vaičiūtė

Biological Sciences Faculty Publications

Retrieval of the phycocyanin concentration (PC), a characteristic pigment of, and proxy for, cyanobacteria biomass, from hyperspectral satellite remote sensing measurements is challenging due to uncertainties in the remote sensing reflectance (∆R_rs) resulting from atmospheric correction and instrument radiometric noise. Although several individual algorithms have been proven to capture local variations in cyanobacteria biomass in specific regions, their performance has not been assessed on hyperspectral images from satellite sensors. Our work leverages a machine-learning model, Mixture Density Networks (MDNs), trained on a large (N = 939) dataset of collocated in situ chlorophyll-a concentrations (Chla), …

Estimating Wildlife Strike Costs At Us Airports: A Machine Learning Approach, Levi Altringer, Jordan Navin, Michael J. Begier, Stephanie A. Shwiff, Aaron M. Anderson

USDA Wildlife Services: Staff Publications

Current lower bound estimates of the economic burden of wildlife strikes make use of mean cost assignment to impute missing values in the National Wildlife Strike Database (NWSD). The accuracy of these estimates, however, are undermined by the skewed nature of reported cost data and fail to account for differences in observed strike characteristics—e.g., type of aircraft, size of aircraft, type of damage, size of animal struck, etc. This paper makes use of modern machine learning techniques to provide a more accurate measure of the strike-related costs that accrue to the US civil aviation industry. We estimate that wildlife strikes …

The Future Of Zoonotic Risk Prediction, Colin J. Carlson, Maxwell J. Farrell, Zoe Grange, Barbara A. Han, Nardus Mollentze, Alexandra L. Phelan, Angela L. Rasmussen, Gregory F. Albery, Bernard Bett, David Brett-Major, Lily E. Cohen, Tad Dallas, Evan A. Eskew, Anna C. Fagre, Kristian M. Forbes, Rory Gibb, Sam Halabi, Charlotte C. Hammer, Rebecca Katz, Jason Kindrachuk, Renata L. Muylaert, Felicia B. Nutter, Joseph Ogola, Kevin J. Olival, Michelle Rourke, Sadie J. Ryan, Noam Ross, Stephanie N. Seifert, Tarja Sironen, Claire J. Standley, Kishana Taylor, Marietjie Venter, Paul W. Webala

Journal Articles: Epidemiology

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open …

Predictive Model And Feature Importance For Early Detection Of Type Ii Diabetes Mellitus, Eric Adua, Emmanuel A. Kolog, Ebenezer Afrifa-Yamoah, Bright Amankwah, Christian Obirikorang, Enoch O. Anto, Emmanuel Acheampong, Wei Wang, Antonia Y. Tetteh

Research outputs 2014 to 2021

Background: Accurate prediction and early recognition of type II diabetes (T2DM) will lead to timely and meaningful interventions, while preventing T2DM associated complications. In this context, machine learning (ML) is promising, as it can transform vast amount of T2DM data into clinically relevant information. This study compares multiple ML techniques for predictive modelling based on different T2DM associated variables in an African population, Ghana. Methods: The study involved 219 T2DM patients and 219 healthy individuals who were recruited from the hospital and the local community, respectively. Anthropometric and biochemical information including glycated haemoglobin (HbA1c), body mass index (BMI), blood pressure, …

A Comprehensive Review On Medical Diagnosis Using Machine Learning, Kaustubh Arun Bhavsar, Ahed Abugabah, Jimmy Singla, Ahmad Ali Alzubi, Ali Kashif Bashir, Nikita

All Works

The unavailability of sufficient information for proper diagnosis, incomplete or miscommunication between patient and the clinician, or among the healthcare professionals, delay or incorrect diagnosis, the fatigue of clinician, or even the high diagnostic complexity in limited time can lead to diagnostic errors. Diagnostic errors have adverse effects on the treatment of a patient. Unnecessary treatments increase the medical bills and deteriorate the health of a patient. Such diagnostic errors that harm the patient in various ways could be minimized using machine learning. Machine learning algorithms could be used to diagnose various diseases with high accuracy. The use of machine …

Adaptive Physics-Based Non-Rigid Registration For Immersive Image-Guided Neuronavigation Systems, Fotis Drakopoulos, Christos Tsolakis, Angelos Angelopoulos, Yixun Liu, Chengjun Yao, Kyriaki Rafailia Kavazidi, Nikolaos Foroglou, Andrey Fedorov, Sarah Frisken, Ron Kikinis, Alexandra Golby, Nikos Chrisochoides

Computer Science Faculty Publications

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A …

Disrupting The Perioperative Opioid Gateway: Identification Of Risk Factors For New Persistent Post-Surgical Opioid Use, Gia Marie Pittet

All ETDs from UAB

A large portion of the American Opioid Crisis is due to opioid naïve patients who become new persistent post-surgical opioid users, although the risk factors for the development of this addiction are not well studied. The objective of this study was to analyze multiple layers of pre-operative and procedural risk factors using an ecological perspective theoretical framework in adult patients undergoing invasive surgery. We performed a retrospective analysis of 13,970 opioid naïve adults in a mixed surgical cohort with data available at the University of California Los Angeles that was merged with narcotics data for the State of California (IRB#19-000625). …

Simultaneous Wound Border Segmentation And Tissue Classification Using A Conditional Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Manisa Pipattanasomporn, Ozgur Guler

Engineering Technology Faculty Publications

Generative adversarial network (GAN) applications on medical image synthesis have the potential to assist caregivers in deciding a proper chronic wound treatment plan by understanding the border segmentation and the wound tissue classification visually. This study proposes a hybrid wound border segmentation and tissue classification method utilising conditional GAN, which can mimic real data without expert knowledge. We trained the network on chronic wound datasets with different sizes. The performance of the GAN algorithm is evaluated through the mean squared error, Dice coefficient metrics and visual inspection of generated images. This study also analyses the optimum number of training images …

Applications Of Longitudinal Machine Learning Methods In Multi-Study Alzheimer's Disease Datasets, Charles F. Murchison

All ETDs from UAB

Advances in statistical learning models for prediction have led to broader application across a variety of disciplines, granting generalizations and adaptations that were previ-ously intractable even with advanced computational techniques. Among these is the al-lowance of correlated data with inherent paneled structure such as longitudinal or clus-tered data; adjustments which have already begun to be applied to a variety of supervised and unsupervised machine learning methods which had previously focused on cross-sec-tional data. These modifications have seen rudimentary testing in a number of applied disciplines where correlated data is commonly observed, including medical and clinical research. One field in particular …

Machine Learning Applications To Neuroimaging For Glioma Detection And Classification: An Artificial Intelligence Augmented Systematic Review, Quinlan D. Buchlak, Nazanin Esmaili, Jean-Christophe Leveque, Christine Bennett, Farrokh Farrokhi, Massimo Piccardi

Medical Papers and Journal Articles

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted …

Applying Machine Learning Approaches To Suicide Prediction Using Healthcare Data: Overview And Future Directions, Edwin D Boudreaux, Elke Rundensteiner, Feifan Liu, Bo Wang, Celine Larkin, Emmanuel Agu, Samiran Ghosh, Joshua Semeter, Gregory Simon, Rachel E Davis-Martin

Journal Articles

No abstract provided.

Machine Learning Models For 6-Month Survival Prediction After Surgical Resection Of Glioblastoma, Jeffrey Gray, Lohit Velagapudi, Michael Baldassari, Bryan Sadler, David Vuong

Phase 1

Introduction: The role of surgical resection for the treatment of glioblastoma multiforme is well established. Survival analysis after resective surgery in the literature comprises mostly of traditional statistical models. Machine learning models offer powerful predictive and analytical capability for varied datasets and offer improved generalizability and scalability. We analyzed survival data of patients with glioblastoma with various machine learning algorithms and compared it to binary logistic regression.

Methods: We retrospectively identified cases of glioblastoma treated with surgical resection at our institution from 2012-2018. Feature scaling and one-hot encoding was used to better fit the models to the data and used …

Readmission Risk Assessment Tool For Stroke Patients, Simran Rahi, Sasha Mitts, Dominick Battistini, Tiffany D’Souza, Bryan Sadler, Krista Mar, Maureen Deprince, Deborah Murphy, Diana Tzeng, Md

Phase 1

Introduction: Strokes are one of the leading causes of morbidity and mortality in the world and its cost of management has vastly increased; an effective prediction tool that utilizes artificial intelligence to lower the rate of stroke-related readmissions has the potential to lower healthcare costs and increase the quality of provider care. We hypothesize that machine learning techniques are superior to traditional statistics when determining the likelihood of 30-day readmission for Jefferson’s stroke patients.

Methods: Jefferson’s existing data on stroke patients were cleaned, aggregated, and prepared to be split into train and test sets. Using the train sets, machine learning …