126 research outputs found
Artificial intelligence and automation in valvular heart diseases
Artificial intelligence (AI) is gradually changing every aspect of social life, and healthcare is no exception. The clinical procedures that were supposed to, and could previously only be handled by human experts can now be carried out by machines in a more accurate and efficient way. The coming era of big data and the advent of supercomputers provides great opportunities to the development of AI technology for the enhancement of diagnosis and clinical decision-making. This review provides an introduction to AI and highlights its applications in the clinical flow of diagnosing and treating valvular heart diseases (VHDs). More specifically, this review first introduces some key concepts and subareas in AI. Secondly, it discusses the application of AI in heart sound auscultation and medical image analysis for assistance in diagnosing VHDs. Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery. This review also describes the state-of-the-art autonomous surgical robots and their roles in cardiac surgery and intervention
Radiomics Feature Analysis Using Native T1 Mapping for Discriminating Between Cardiac Tumors and Thrombi
Rationale and objectives: Accurate differential diagnosis is essential because cardiac tumors and thrombi have different prognoses and therapeutic approaches. Native T1 map provides an objective T1 time quantifications of cardiac mass without the need for a contrast agent. We examined the diagnostic performance of radiomics features for differentiating cardiac tumors from thrombi using cardiac magnetic resonance imaging T1 mapping technique compared to that of late gadolinium enhancement (LGE) imaging.
Materials and methods: This retrospective study included 22 cardiac tumors and 21 thrombi of 41 patients who underwent cardiac magnetic resonance imaging from December 2013 to May 2018. Fifty-six radiomics features were extracted from native T1 images. The least absolute shrinkage and selection operator method was used for feature selection and rad score extraction. The diagnostic performance of the rad score was compared to that of the native T1 value (mean T1) and LGE ratio.
Results: The area under the receiver operating characteristic curve of the rad score was higher than that of the mean T1 and LGE ratio (0.98 vs. 0.86 vs. 0.82, p = 0.001). With the optimal cut-off value, the rad score showed sensitivity, specificity, and accuracy of 95.4%, 95.2%, and 95.2%, respectively. Combination of the rad score and mean T1 showed a significantly higher diagnostic performance than mean T1 (p = 0.019) or LGE ratio (p = 0.022).
Conclusion: The rad score derived from native T1 maps can differentiate thrombi from tumors better than the mean T1 or LGE ratio. This is valuable for determining a treatment strategy for cardiac lesions in patients who cannot tolerate contrast agents.ope
Automated Analysis of 3D Stress Echocardiography
__Abstract__
The human circulatory system consists of the heart, blood, arteries, veins and
capillaries. The heart is the muscular organ which pumps the blood through the
human body (Fig. 1.1,1.2). Deoxygenated blood flows through the right atrium
into the right ventricle, which pumps the blood into the pulmonary arteries. The
blood is carried to the lungs, where it passes through a capillary network that
enables the release of carbon dioxide and the uptake of oxygen. Oxygenated
blood then returns to the heart via the pulmonary veins and flows from the left
atrium into the left ventricle. The left ventricle then pumps the blood through the
aorta, the major artery which supplies blood to the rest of the body [Drake et a!.,
2005; Guyton and Halt 1996]. Therefore, it is vital that the cardiovascular system
remains healthy. Disease of the cardiovascular system, if untreated, ultimately
leads to the failure of other organs and death
Automated analysis of 3D echocardiography
In this thesis we aim at automating the analysis of 3D echocardiography, mainly targeting the functional analysis of the left ventricle. Manual analysis of these data is cumbersome, time-consuming and is associated with inter-observer and inter-institutional variability. Methods for reconstruction of 3D echocardiographic images from fast rotating ultrasound transducers is presented and methods for analysis of 3D echocardiography in general, using tracking, detection and model-based segmentation techniques to ultimately fully automatically segment the left ventricle for functional analysis. We show that reliable quantification of left ventricular volume and mitral valve displacement can be achieved using the presented techniques.SenterNovem (IOP Beeldverwerking, grant IBVC02003), Dutch Technology Foundation STW (grant 06666)UBL - phd migration 201
ASNC/AHA/ASE/EANM/HFSA/ISA/SCMR/SNMMI Expert Consensus Recommendations for Multimodality Imaging in Cardiac Amyloidosis:Part 1 of 2-Evidence Base and Standardized Methods of Imaging
In the Introduction SCMR was listed incorrectly. SCMR is the Society for Cardiovascular Magnetic Resonance. âą Figure 1 erroneously printed without Yen sign („) in âFinal Diagnosis.â Please see revised Figure 1. âą Acknowledgments erroneously printed without reviewers Richard Cheng, MD and Roy John, MD
Automated Echocardiographic Image Interpretation Using Artificial Intelligence
In addition to remaining as one of the leading causes of global mortality, cardio vascular disease has a significant impact on overall health, well-being, and life expectancy. Therefore, early detection of anomalies in cardiac function has become essential for early treatment, and therefore reduction in mortalities. Echocardiography is the most commonly used modality for evaluating the structure and function of the heart. Analysis of echocardiographic images has an important role in the clinical practice in assessing the cardiac morphology and function and thereby reaching a diagnosis. The process of interpretation of echocardiographic images is considered challenging for several reasons. The manual annotation is still a daily work in the clinical routine due to the lack of reliable automatic interpretation methods. This can lead to time-consuming tasks that are prone to intra- and inter-observer variability. Echocardiographic images inherently suffer from a high level of noise and poor qualities. Therefore, although several studies have attempted automating the process, this re-mains a challenging task, and improving the accuracy of automatic echocardiography interpretation is an ongoing field. Advances in Artificial Intelligence and Deep Learning can help to construct an auto-mated, scalable pipeline for echocardiographic image interpretation steps, includingview classification, phase-detection, image segmentation with a focus on border detection, quantification of structure, and measurement of the clinical markers. This thesis aims to develop optimised automated methods for the three individual steps forming part of an echocardiographic exam, namely view classification, left ventricle segmentation, quantification, and measurement of left ventricle structure. Various Neural Architecture Search methods were employed to design efficient neural network architectures for the above tasks. Finally, an optimisation-based speckle tracking echocardiography algorithm was proposed to estimate the myocardial tissue velocities and cardiac deformation. The algorithm was adopted to measure cardiac strain which is used for detecting myocardial ischaemia. All proposed techniques were compared with the existing state-of-the-art methods. To this end, publicly available patients datasets, as well as two private datasets provided by the clinical partners to this project, were used for developments and comprehensive performance evaluations of the proposed techniques. Results demonstrated the feasibility of using automated tools for reliable echocardiographic image interpretations, which can be used as assistive tools to clinicians in obtaining clinical measurements
Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging
Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET).
Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools â by tracking the heartâs kinetic activity using micro-sized MEMS sensors â and novel computational approaches â by deploying signal processing and machine learning techniquesâfor detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations.
Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes.
Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien kÀyttö sydÀnkardiografiassa sekÀ lÀÀketieteellisessÀ 4D-kuvantamisessa
Tausta: SydÀn- ja verisuonitaudit ovat yleisin kuolinsyy. NÀistÀ kuolemantapauksista lÀhes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron hÀiriöistÀ. Moniulotteiset mikroelektromekaaniset jÀrjestelmÀt (MEMS) mahdollistavat sydÀnlihaksen mekaanisen liikkeen mittaamisen, mikÀ puolestaan tarjoaa tÀysin uudenlaisen ja innovatiivisen ratkaisun sydÀmen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjÀrjestelmien kÀyttÀmisen sydÀmen toiminnan tutkimuksessa sekÀ lÀÀketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa.
MenetelmÀt: TÀmÀ vÀitöskirjatyö esittelee uuden sydÀmen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien kÀyttöön. Uudet laskennalliset lÀhestymistavat, jotka perustuvat signaalinkÀsittelyyn ja koneoppimiseen, mahdollistavat sydÀmen patologisten hÀiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. TÀssÀ tutkimuksessa keskitytÀÀn erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). NÀiden tekniikoiden avulla voidaan mitata kardiorespiratorisen jÀrjestelmÀn mekaanisia ominaisuuksia.
Tulokset: Kokeelliset analyysit osoittivat, ettÀ integroimalla usean sensorin dataa voidaan mitata syketiheyttÀ 99% (terveillÀ n=29) tarkkuudella, havaita sydÀmen rytmihÀiriöt (n=435) 95-97%, tarkkuudella, sekÀ havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). LisÀksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydÀmen 4D PET-kuvan laatua, kun liikeepÀtarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillÀ, n=9) osoitti lupaavia tuloksia sydÀnsykkeen ajoituksen ja intervallien sekÀ sydÀnlihasmuutosten mittaamisessa.
PÀÀtelmÀ: TÀmÀn tutkimuksen tulokset osoittavat, ettÀ kardiologisilla MEMS-liikeantureilla on kliinistÀ potentiaalia sydÀmen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistÀÀ eteisvÀrinÀn (AFib), sydÀninfarktin (MI) ja CAD:n havaitsemista. LisÀksi MEMS-liiketunnistus parantaa sydÀmen PET-kuvantamisen luotettavuutta ja laatua
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 164
This bibliography lists 275 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1977
Three-dimensional transoesophageal echocardiography: how to use and when to use-a clinical consensus statement from the European Association of Cardiovascular Imaging of the European Society of Cardiology.
peer reviewedThree-dimensional transoesophageal echocardiography (3D TOE) has been rapidly developed in the last 15 years. Currently, 3D TOE is particularly useful as an additional imaging modality for the cardiac echocardiographers in the echo-lab, for cardiac interventionalists as a tool to guide complex catheter-based procedures cardiac, for surgeons to plan surgical strategies, and for cardiac anaesthesiologists and/or cardiologists, to assess intra-operative results. The authors of this document believe that acquiring 3D data set should become a 'standard part' of the TOE examination. This document provides (i) a basic understanding of the physic of 3D TOE technology which enables the echocardiographer to obtain new skills necessary to acquire, manipulate, and interpret 3D data sets, (ii) a description of valvular pathologies, and (iii) a description of non-valvular pathologies in which 3D TOE has shown to be a diagnostic tool particularly valuable. This document has a new format: instead of figures randomly positioned through the text, it has been organized in tables which include figures. We believe that this arrangement makes easier the lecture by clinical cardiologists and practising echocardiographers
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 218, April 1981
This bibliography lists 161 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1981
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