Basic Science to Clinical Research: Segmentation of Ultrasound and Modelling in Clinical Informatics

The world of basic science is a world of minutia; it boils down to improving even a fraction of a percent over the baseline standard. It is a domain of peer reviewed fractions of seconds and the world of squeezing every last ounce of efficiency from a processor, a storage medium, or an algorithm. The field of health data is based on extracting knowledge from segments of data that may improve some clinical process or practice guideline to improve the time and quality of care. Clinical informatics and knowledge translation provide this information in order to reveal insights to the world of improving patient treatments, regimens, and overall outcomes. In my world of minutia, or basic science, the movement of blood served an integral role. The novel detection of sound reverberations map out the landscape for my research. I have applied my algorithms to the various anatomical structures of the heart and artery system. This serves as a basis for segmentation, active contouring, and shape priors. The algorithms presented, leverage novel applications in segmentation by using anatomical features of the heart for shape priors and the integration of optical flow models to improve tracking. The presented techniques show improvements over traditional methods in the estimation of left ventricular size and function, along with plaque estimation in the carotid artery. In my clinical world of data understanding, I have endeavoured to decipher trends in Alzheimer’s disease, Sepsis of hospital patients, and the burden of Melanoma using mathematical modelling methods. The use of decision trees, Markov models, and various clustering techniques provide insights into data sets that are otherwise hidden. Finally, I demonstrate how efficient data capture from providers can achieve rapid results and actionable information on patient medical records. This culminated in generating studies on the burden of illness and their associated costs. A selection of published works from my research in the world of basic sciences to clinical informatics has been included in this thesis to detail my transition. This is my journey from one contented realm to a turbulent one

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Transapikalinės mitralinio vožtuvo korekcijos skaitinis modeliavimas

This dissertation presents the numerical modeling approach for the simulation of transapical mitral valve (MV) repair procedure. The main object of the research is the development of the finite element (FE) model of the MV with ruptured chordae tendineae and its application for modeling of MV repair with neochordae implantation through the transapical approach. The dissertation aims to develop and implement a numerical model of the MV for quantitative evaluation of transapical MV repair surgical procedure and its effect on post-operative MV function. The work presents five tasks. Firstly, studies describing computational models used for investigation of MV biomechanical functions and evaluation of novel MV repair surgical techniques are reviewed. Next, the modeling strategy for the numerical simulation of virtual transapical MV repair procedure is developed. Patient-specific echocardiographic image data are obtained for the reconstruction of MV geometry and creation of structural FE model with MV prolapse. Virtual repair using different neochordal lengths is performed and the systolic function of the MV model is simulated. Finally, the outcomes of virtual transapical MV repair are evaluated and the eligibility of numerical modeling strategy is considered. The present thesis consists of an introduction, three main chapters, general conclusions, references, a list of publications by the author on the topic of the dissertation and a summary in Lithuanian. The introduction presents the research problem, the relevance of the thesis, the object of the research, formulates the aim and the tasks of the work, describes the research methodology and scientific novelty, considers the practical significance of the results and the defensive statements. Chapter 1 discusses the problem of MV prolapse from both medical and mechanical point of view. In the medical part, the anatomy and physiology of the human heart are described and thorough analysis of the MV structure is presented. In the mechanical part, an overview of studies describing computational MV models is provided and the models analyzing different MV repair techniques are distinguished. Chapter 2 introduces the modeling strategy applied for virtual transapical MV repair and its mathematical formulation. Chapter 3 presents the systolic function simulations of virtual repair procedures using two sets of patient-specific data and evaluates the parameters calculated during these simulations before and after virtual repair. The results of this dissertation were published in 4 scientific papers: two articles in journals with impact factor indexed in Clarivate Analytics Web of Science database, one article in a journal indexed in other international databases and one paper in international conference proceedings. These results were presented at 7 international conferences.Dissertatio

A framework for analysis of linear ultrasound videos to detect fetal presentation and heartbeat.

Confirmation of pregnancy viability (presence of fetal cardiac activity) and diagnosis of fetal presentation (head or buttock in the maternal pelvis) are the first essential components of ultrasound assessment in obstetrics. The former is useful in assessing the presence of an on-going pregnancy and the latter is essential for labour management. We propose an automated framework for detection of fetal presentation and heartbeat from a predefined free-hand ultrasound sweep of the maternal abdomen. Our method exploits the presence of key anatomical sonographic image patterns in carefully designed scanning protocols to develop, for the first time, an automated framework allowing novice sonographers to detect fetal breech presentation and heartbeat from an ultrasound sweep. The framework consists of a classification regime for a frame by frame categorization of each 2D slice of the video. The classification scores are then regularized through a conditional random field model, taking into account the temporal relationship between the video frames. Subsequently, if consecutive frames of the fetal heart are detected, a kernelized linear dynamical model is used to identify whether a heartbeat can be detected in the sequence. In a dataset of 323 predefined free-hand videos, covering the mother's abdomen in a straight sweep, the fetal skull, abdomen, and heart were detected with a mean classification accuracy of 83.4%. Furthermore, for the detection of the heartbeat an overall classification accuracy of 93.1% was achieved

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Quantification of right and left ventricular function with real-time three-dimensional ultrasound

Presents a study for validation of real-time three-dimensional ultrasound as a diagnostic tool in patients with primary pulmonary hypertension. Dynamic analysis of the heart is performed via quantification of right and left ventricle volumes. Segmentation of ventricular cavities is performed via a deformable-model for a set of denoised frames spread over an entire cardiac cycle. Spatio-temporal denoising is carried out by brushlet analysis that has been optimized by incorporating space and time coherence. in. texture characterization. Quantitative measures for right and left ventricular ejection fraction are compared to clinical MRI of the same patients