Biomedical Engineering and Bioengineering Commons^™

Towards Patient Specific Mitral Valve Modelling Via Dynamic 3d Transesophageal Echocardiography, Patrick Carnahan

Electronic Thesis and Dissertation Repository

Mitral valve disease is a common pathologic problem occurring increasingly in an aging population, and many patients suffering from mitral valve disease require surgical intervention. Planning an interventional approach from diagnostic imaging alone remains a significant clinical challenge. Transesophageal echocardiography (TEE) is the primary imaging modality used diagnostically, it has limitations in image quality and field-of-view. Recently, developments have been made towards modelling patient-specific deformable mitral valves from TEE imaging, however, a major barrier to producing accurate valve models is the need to derive the leaflet geometry through segmentation of diagnostic TEE imaging. This work explores the development of volume …

Deep Learning And Generative Ai Approaches For Automated Diagnosis And Personalized Treatment: Bridging Machine Learning, Medicine, And Biomechanics In Predicting Tissue Mechanics And Biomaterial Properties., Yasin Shokrollahi

Theses and Dissertations

Machine learning, particularly deep neural networks, has demonstrated significant potential in predicting high-dimensional tasks across various domains. This work encompasses a detailed review of Generative AI in healthcare and three studies integrating machine learning with finite element analysis for predicting biomechanical behaviors and properties. Initially, we provide a comprehensive overview of Generative AI applications in healthcare, focusing on Transformers and Denoising Diffusion models and suggesting potential research avenues to address existing challenges.

Subsequently, we addressed soccer-related ocular injuries by combining finite element analysis and machine learning to predict retinal mechanics following a soccer ball hit rapidly. The prediction errors are …

Enhancing Human Key Point Identification: A Comparative Study Of High-Resolution Vicon Dataset And Coco Dataset Using Bpnet, Bibash Lama

Masters Theses

Accurately identifying human key points is crucial for various applications, including activity recognition, pose estimation, and gait analysis. This study presents a high-resolution dataset created using the VICON motion capture system and three differently oriented 2D cameras, that can be used to train different neural networks for estimating the 2D key joint positions of the person from the 2D images or videos. The participants in the study included 25 healthy adults (17 males and 8 females) performing normal gait movements for about 2 to 3 seconds. The VICON system captured 3D ground truth data, while the three 2D cameras collected …

An Automated, Deep Learning Approach To Systematically & Sequentially Derive Three-Dimensional Knee Kinematics Directly From Two-Dimensional Fluoroscopic Video, Viet Dung Nguyen

Doctoral Dissertations

Total knee arthroplasty (TKA), also known as total knee replacement, is a surgical procedure to replace damaged parts of the knee joint with artificial components. It aims to relieve pain and improve knee function. TKA can improve knee kinematics and reduce pain, but it may also cause altered joint mechanics and complications. Proper patient selection, implant design, and surgical technique are important for successful outcomes. Kinematics analysis plays a vital role in TKA by evaluating knee joint movement and mechanics. It helps assess surgery success, guides implant and technique selection, informs implant design improvements, detects problems early, and improves patient …

Visual Complexity Of The Time-Frequency Image Pinpoints The Epileptogenic Zone: An Unsupervised Deep-Learning Tool To Analyze Interictal Intracranial Eeg, Sarvagya Gupta

Graduate Masters Theses

Epilepsy, a prevalent neurological disorder characterized by recurrent seizures, continues to pose significant challenges in diagnosis and treatment, particularly among children. Despite substantial advancements in medical technology and treatment modalities, localization of the part of brain that causes seizures (Epileptogenic Zone) remains a difficult task. Intracranial EEG (iEEG) is often used to estimate the epileptogenic zone (EZ) in children with drugresistant epilepsy (DRE) and target it during surgery. Conventionally, iEEG signals are inspected in the time domain by human experts aiming to locate epileptiform activity.

Visual scrutiny of the iEEG time-frequency (TF) images can be an alternative way to review …

Breast Density Classification Using Deep Learning, Conrad Thomas Testagrose

UNF Graduate Theses and Dissertations

Breast density screenings are an accepted means to determine a patient's predisposed risk of breast cancer development. Although the direct correlation is not fully understood, breast cancer risk increases with higher levels of mammographic breast density. Radiologists visually assess a patient's breast density using mammogram images and assign a density score based on four breast density categories outlined by the Breast Imaging and Reporting Data Systems (BI-RADS). There have been efforts to develop automated tools that assist radiologists with increasing workloads and to help reduce the intra- and inter-rater variability between radiologists. In this thesis, I explored two deep-learning-based approaches …