Engineering Commons^™

Evaluating Neuroimaging Modalities In The A/T/N Framework: Single And Combined Fdg-Pet And T1-Weighted Mri For Alzheimer’S Diagnosis, Peiwang Liu

McKelvey School of Engineering Theses & Dissertations

With the escalating prevalence of dementia, particularly Alzheimer's Disease (AD), the need for early and precise diagnostic techniques is rising. This study delves into the comparative efficacy of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and T1-weighted Magnetic Resonance Imaging (MRI) in diagnosing AD, where the integration of multimodal models is becoming a trend. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we employed linear Support Vector Machines (SVM) to assess the diagnostic potential of these modalities, both individually and in combination, within the AD continuum. Our analysis, under the A/T/N framework's 'N' category, reveals that FDG-PET consistently outperforms T1w-MRI across …

Investigating Applications Of Deep Learning For Diagnosis Of Post Traumatic Elbow Disease, Hugh James

McKelvey School of Engineering Theses & Dissertations

Traumatic events such as dislocation, breaks, and arthritis of musculoskeletal joints can cause the development of post-traumatic joint contracture (PTJC). Clinically, noninvasive techniques such as Magnetic Resonance Imaging (MRI) scans are used to analyze the disease. Such procedures require a patient to sit sedentary for long periods of time and can be expensive as well. Additionally, years of practice and experience are required for clinicians to accurately recognize the diseased anterior capsule region and make an accurate diagnosis. Manual tracing of the anterior capsule is done to help with diagnosis but is subjective and timely. As a result, there is …

Development Of The Assessment Of Clinical Prediction Model Transportability (Apt) Checklist, Sean Chonghwan Yu

McKelvey School of Engineering Theses & Dissertations

Clinical Prediction Models (CPM) have long been used for Clinical Decision Support (CDS) initially based on simple clinical scoring systems, and increasingly based on complex machine learning models relying on large-scale Electronic Health Record (EHR) data. External implementation – or the application of CPMs on sites where it was not originally developed – is valuable as it reduces the need for redundant de novo CPM development, enables CPM usage by low resource organizations, facilitates external validation studies, and encourages collaborative development of CPMs. Further, adoption of externally developed CPMs has been facilitated by ongoing interoperability efforts in standards, policy, and …

Application Of Crowdsourcing And Machine Learning To Predict Sentiments In Textual Student Feedback In Large Computer Science Classes, Robert Kasumba

McKelvey School of Engineering Theses & Dissertations

With the increasing enrollment numbers into popular computer science courses, there is a need to bridge the similarly increasing feedback gap between individual students and course instructors. One way to address this challenge is for instructors to collect feedback from students in form of textual reviews or unit-of-study reflections – however, manually reading these reviews is time-consuming, and self-reported Likert scale responses are noisy. Rule-based approaches to sentiment analysis such as VADER (Valence Aware Dictionary and sEntiment Reasoner) have been used to capture the sentiments conveyed in textual feedback, they however fail to capture contextual differences as many words have …

Predicting Patient Outcomes With Machine Learning For Diverse Health Data, Dingwen Li

McKelvey School of Engineering Theses & Dissertations

As digitized clinical and health data become ubiquitous, machine learning techniques have shown promise in predicting various clinical outcomes. In this thesis research, we exploit three types of data including (1) data collected through wearables outside hospitals, (2) electronic health records (EHR) data of inpatient in general hospital wards, (3) intraoperative data collected during surgery. This thesis work investigates machine learning approaches for the diverse clinical and health data with distinctive characteristics and challenges in the context of real-world clinical applications. Specifically, this thesis makes the following contributions to the state of the art of clinical machine learning.

Extracting informative …

Using Computer Vision To Track Anatomical Structures During Cochlear Implant Surgery, Nicholas Bach

McKelvey School of Engineering Theses & Dissertations

There is a steep learning curve for surgeons performing cochlear implant surgeries. We aimed to use computer vision to track anatomical features with the goal of helping surgeons perform cochlear implant surgery without damaging the cochlea. We compared nine algorithms in total, seven object tracking algorithms and two optical flow algorithms utilizing the LucasKanade method, on manually created cochlear implant surgery videos to determine the accuracy associated with each. Compared with eight other algorithms, we observed that an iterative pyramidal implementation of the Lucas-Kanade (IPLK) method, implemented through OpenCV, performed the best. The IPLK method had the lowest error rate …

Continuous-Time And Complex Growth Transforms For Analog Computing And Optimization, Oindrila Chatterjee

McKelvey School of Engineering Theses & Dissertations

Analog computing is a promising and practical candidate for solving complex computational problems involving algebraic and differential equations. At the fundamental level, an analog computing framework can be viewed as a dynamical system that evolves following fundamental physical principles, like energy minimization, to solve a computing task. Additionally, conservation laws, such as conservation of charge, energy, or mass, provide a natural way to couple and constrain spatially separated variables. Taking a cue from these observations, in this dissertation, I have explored a novel dynamical system-based computing framework that exploits naturally occurring analog conservation constraints to solve a variety of optimization …

Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi

McKelvey School of Engineering Theses & Dissertations

A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …

Wheelchair Propulsion For Everyday Manual Wheelchair Users: Repetition Training And Machine Learning-Based Monitoring, Pin-Wei Chen

Arts & Sciences Electronic Theses and Dissertations

Upper limb pain and injuries are prevalent among manual wheelchair users and can restrict their participation and daily activities. Due to the high repetition and force in wheelchair propulsion, chronic wheelchair propulsion has been linked to the risk of upper limb pain and injury. Prevention of upper limb pain and injury is a high priority in wheelchair-related research. Decades of research in wheelchair propulsion biomechanics have led to clinical practice guidelines (CPG). Unfortunately, a decade after the publication of the CPG, CPG-recommended propulsion is still uncommon. Hence, for the first aim, a randomized controlled trial pilot study with two groups …

Differential Estimation Of Audiograms Using Gaussian Process Active Model Selection, Trevor Larsen

McKelvey School of Engineering Theses & Dissertations

Classical methods for psychometric function estimation either require excessive resources to perform, as in the method of constants, or produce only a low resolution approximation of the target psychometric function, as in adaptive staircase or up-down procedures. This thesis makes two primary contributions to the estimation of the audiogram, a clinically relevant psychometric function estimated by querying a patient’s for audibility of a collection of tones. First, it covers the implementation of a Gaussian process model for learning an audiogram using another audiogram as a prior belief to speed up the learning procedure. Second, it implements a use case of …

Visualization Of Deep Convolutional Neural Networks, Dingwen Li

McKelvey School of Engineering Theses & Dissertations

Deep learning has achieved great accuracy in large scale image classification and scene recognition tasks, especially after the Convolutional Neural Network (CNN) model was introduced. Although a CNN often demonstrates very good classification results, it is usually unclear how or why a classification result is achieved. The objective of this thesis is to explore several existing visualization approaches which offer intuitive visual results. The thesis focuses on three visualization approaches: (1) image masking which highlights the region of image with high influence on the classification, (2) Taylor decomposition back-propagation which generates a per pixel heat map that describes each pixel's …