Machine Learning for Biomedical Application

Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

Washington University Senior Undergraduate Research Digest (WUURD), Spring 2018

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Scienc

Atherosclerotic Plaque Characterization in Humans with Acoustic Radiation Force Impulse (ARFI) Imaging

Cardio- and cerebrovascular diseases (CVD) are among the leading causes of death and disability in the United States. A vast majority of heart attacks and strokes are linked to atherosclerosis; a condition characterized by inflammation and plaque accumulation in the arterial wall that can rupture and propagate an acute thrombotic event. Identification of plaques that are vulnerable to rupture is paramount to the prevention of heart attacks and strokes, but a noninvasive plaque characterization imaging technology that is cost-effective, safe, and accurate has remained elusive. The goal of this dissertation is to evaluate whether acoustic radiation force impulse (ARFI) imaging, an ultrasound-based elastography technique, can noninvasively characterize plaque components and identify features that have been shown to correlate with plaque vulnerability. Data are presented from preclinical studies, done in a porcine model of atherosclerosis, and clinical studies, performed in patients undergoing carotid endarterectomy (CEA), to demonstrate the sensitivity and specificity of ARFI for various plaque components. Additionally, the ability of ARFI to measure fibrous cap thickness is assessed with finite element method (FEM) modelling, and the limits of ARFI fibrous cap resolution are analyzed. Lastly, advanced ARFI-based plaque imaging methods are explored, including intravascular ARFI for coronary plaque characterization. Overall, these studies demonstrate that ARFI can delineate features consistent with vulnerable plaque in a clinical imaging context and suggest that ARFI has the potential to improve the current state of the art in atherosclerosis diagnostics.Doctor of Philosoph

Vision-based estimation of volume status in ultrasound

This thesis provides a proof-of-concept approach to the analysis of ultrasound imagery using machine learning and computer vision for the purposes of tracking relative changes in apparent circulating blood volume. Data for the models was collected from a simulation which involved having healthy subjects recline at angles between 0 and 90 degrees to induce changes in the size of the internal jugular vein (IJV) resulting from gravity. Ultrasound video clips were then captured of the IJV. The clips were segmented, followed by feature generation, feature selection and training of predictive models to determine the angle of inclination. This research provides insight into the feasibility of using automated analysis techniques to enhance portable ultrasound as a monitoring tool. In a dataset of 34 subjects the angle was predicted within 11 degrees. An accuracy of 89% was achieved for high/low classification

Application-Dependent Wavelength Selection For Hyperspectral Imaging Technologies

Hyperspectral imaging has proven to provide benefits in numerous application domains, including agriculture, biomedicine, remote sensing, and food quality management. Unlike standard color imagery composed of these broad wavelength bands, hyperspectral images are collected over numerous (possibly hundreds) of narrow wavelength bands, thereby offering vastly more information content than standard imagery. It is this higher information content which enables improved performance in complex classification and regression tasks. However, this successful technology is not without its disadvantages which include high cost, slow data capture, high data storage requirements, and computational complexity. This research seeks to overcome these disadvantages through the development of algorithms and methods to enable the benefits of hyperspectral imaging in inexpensive portable devices that collect spectral data at only a handful (i.e., 5-7) of wavelengths specifically selected for the application of interest.This dissertation focuses on two applications of practical interest: fish fillet species classification for the prevention of food fraud and tissue oxygenation estimation for wound monitoring. Genetic algorithm, self-organizing map, and simulated annealing approaches for wavelength selection are investigated for the first application, combined with common machine learning classifiers for species classification. The simulated annealing approach for wavelength selection is carried over to the wound monitoring application and is combined with the Extended Modified Lambert-Beer law, a tissue oxygenation method that has proven to be robust to differences in melanin concentrations. Analyses for this second application included spectral convolutions to represent data collection with the envisioned inexpensive portable devices. Results of this research showed that high species classification accuracy (\u3e 90%) and low tissue oxygenation error (\u3c 1%) is achievable with just 5-7 selected wavelengths. Furthermore, the proposed wavelength selection and estimation algorithms for the wound monitoring application were found to be robust to variations in the peak wavelength and relatively wide bandwidths of the types of LEDs that may be featured in the designs of such devices

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on

Ultrasound Imaging Innovations for Visualization and Quantification of Vascular Biomarkers

The existence of plaque in the carotid arteries, which provide circulation to the brain, is a known risk for stroke and dementia. Alas, this risk factor is present in 25% of the adult population. Proper assessment of carotid plaque may play a significant role in preventing and managing stroke and dementia. However, current plaque assessment routines have known limitations in assessing individual risk for future cardiovascular events. There is a practical need to derive new vascular biomarkers that are indicative of cardiovascular risk based on hemodynamic information. Nonetheless, the derivation of these biomarkers is not a trivial technical task because none of the existing clinical imaging modalities have adequate time resolution to track the spatiotemporal dynamics of arterial blood flow that is pulsatile in nature. The goal of this dissertation is to devise a new ultrasound imaging framework to measure vascular biomarkers related to turbulent flow, intra-plaque microvasculature, and blood flow rate. Central to the proposed framework is the use of high frame rate ultrasound (HiFRUS) imaging principles to track hemodynamic events at fine temporal resolution (through using frame rates of greater than 1000 frames per second). The existence of turbulent flow and intra-plaque microvessels, as well as anomalous blood flow rate, are all closely related to the formation and progression of carotid plaque. Therefore, quantifying these biomarkers can improve the identification of individuals with carotid plaque who are at risk for future cardiovascular events. To facilitate the testing and the implementation of the proposed imaging algorithms, this dissertation has included the development of new experimental models (in the form of flow phantoms) and a new HiFRUS imaging platform with live scanning and on-demand playback functionalities. Pilot studies were also carried out on rats and human volunteers. Results generally demonstrated the real-time performance and the practical efficacy of the proposed algorithms. The proposed ultrasound imaging framework is expected to improve carotid plaque risk classification and, in turn, facilitate timely identification of at-risk individuals. It may also be used to derive new insights on carotid plaque formation and progression to aid disease management and the development of personalized treatment strategies

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields