The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

Intracoronary electrocardiogram as a direct measure of myocardial ischemia

The electrocardiogram is a valuable diagnostic method providing insight into pathologies of the heart, especially rhythm disorders or insufficient myocardial blood supply (myocardial ischemia). The commonly used surface ECG is, however, limited in detecting short-lasting myocardial ischemia, in particular in the territory of the left circumflex coronary artery supplying the postero-lateral wall of the left ventricle. Conversely, an ECG recorded in close vicinity to the myocardium, i.e., within a coronary artery (intracoronary ECG, icECG) has been thought to overcome these limitations. Since its first implementation during cardiac catheterization in 1985, icECG has shown ample evidence for its diagnostic value given the higher sensitivity for myocardial ischemia detection when compared to the surface ECG. In addition, icECG has been demonstrated to be a direct measure of myocardial ischemia in real-time, thus, providing valuable information during percutaneous coronary diagnostics and interventions. However, a lack of analysing systems to obtain and quantify icECG in real-time discourages routine use. The goals of this MD-PhD thesis are two-fold: First, to determine the diagnostic accuracy of icECG ST-segment shift during pharmacologic inotropic stress in comparison to established indices for coronary lesion severity assessment using quantitative angiographic percent diameter stenosis as reference (Project I). Second, to determine the optimal icECG parameter for myocardial ischemia detection and quantification (Project II and III). In essence, this thesis demonstrates that the icECG is an easy available diagnostic method providing highly accurate information on the amount of myocardial ischemia in real-time. Quantitative assessment of acute, transmural myocardial ischemia by icECG is most accurately performed by measuring ST-segment shift at the J-point, while the quantitative assessment during physical exercise, respectively its pharmacologic simulation, is most accurately performed by measuring ST-segment shift 60ms after the J-point

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

Dynamic Analysis of X-ray Angiography for Image-Guided Coronary Interventions

Percutaneous coronary intervention (PCI) is a minimally-invasive procedure for treating patients with coronary artery disease. PCI is typically performed with image guidance using X-ray angiograms (XA) in which coronary arter

Supervised Deep Learning with Finite Element Synthetic Data for Force Estimation in Robotic-assisted Surgery

The prevalence of robot-assisted minimally invasive surgery on the liver has increased exponentially. Having accurate, real-time knowledge of force during robotic-assisted surgical procedures is vital for safe surgery. Many techniques have been proposed in the literature to tackle this concern, from deploying force sensors to physics-based modeling of the robot and, more recently, learning-based force prediction. For a high-fidelity force measurement, sensors should be integrated at the instrument's tip, close to the surgical site, which brings sterilization, biocompatibility, and MRI compatibility concerns. On the other hand, Dynamic robot modeling may be precise in a specific setting, but it suffers from the lack of generalization encountering unseen settings. Considering the drawbacks and deficits of mentioned methods, indirect force estimation via deflection measurement through imaging techniques is investigated as an alternative solution, generally done via machine learning methods. Almost all previous studies are either supervised learning, where data are labeled with ex-vivo ground truth, or unsupervised or semi-supervised learning, where outcomes are promising but not adequate. This study investigated indirect force prediction for the human liver through a developed deep autoencoder model as a supervised deep learning method trained via synthetic data generated by finite element (FE) simulation. This method took advantage of various patient-specific livers parameters and geometries extracted from CT images. The Hyperelastic modeling of the soft tissue is considered and assessed with various hyperelastic models. The uncertainty due to the surgical tool's occlusion is addressed in this model, and a novel state vector was proposed to improve the accuracy and generalisability of the prediction. In addition, the impact of the bounded region on the model's accuracy was evaluated. It was shown that the proposed method could predict the external force on an unseen tissue with different geometry and mechanical properties. The accuracy of force prediction considering tool occlusion noise diminishes by 4.2 percent, which is in an acceptable range. The accuracy of presented model for various scenarios ranges from 95 to 88 percent. Model's results have been evaluated by predicting the force encountering the surface deformation of an unseen liver geometry and constitutive model where the mean absolute error of prediction is 0.249 Newton

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate

Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks

Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for contextual information extraction and decision making. Beyond modeling advances, the enabler for vehicles to become aware of their surroundings is the availability of visual sensory data, which expand the vehicular perception and realizes vehicular contextual awareness in real-world environments. Research directions for scene understanding pursued by related studies include person/vehicle detection and segmentation, their transition analysis, lane change, and turns detection, among many others Unfortunately, these tasks seem insufficient to completely develop fully-autonomous vehicles i.e. achieving level-5 autonomy, travelling just like human-controlled cars. This latter statement is among the conclusions drawn from this review paper: scene understanding for autonomous driving cars using vision sensors still requires significant improvements. With this motivation, this survey defines, analyzes, and reviews the current achievements of the scene understanding research area that mostly rely on computationally complex deep learning models. Furthermore, it covers the generic scene understanding pipeline, investigates the performance reported by the state-of-the-art, informs about the time complexity analysis of avant garde modeling choices, and highlights major triumphs and noted limitations encountered by current research efforts. The survey also includes a comprehensive discussion on the available datasets, and the challenges that, even if lately confronted by researchers, still remain open to date. Finally, our work outlines future research directions to welcome researchers and practitioners to this exciting domain.This work was supported by the European Commission through European Union (EU) and Japan for Artificial Intelligence (AI) under Grant 957339

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems