64 research outputs found

    Deep Learning in Cardiology

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    The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

    Combinatorial optimisation for arterial image segmentation.

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    Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods

    Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

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    Coronary artery disease is a major cause of morbidity and mortality worldwide. Its underlying histopathology is the atherosclerotic plaque, which comprises lipid, fibrous and—when chronic—calcium components. Intravascular ultrasound (IVUS) and intravascular optical coherence tomography (IVOCT) performed during invasive coronary angiography are reference standards for characterizing the atherosclerotic plaque. Fine image spatial resolution attainable with contemporary coronary computed tomographic angiography (CCTA) has enabled noninvasive plaque assessment, including identifying features associated with vulnerable plaques known to presage acute coronary events. Manual interpretation of IVUS, IVOCT and CCTA images demands scarce physician expertise and high time cost. This has motivated recent research into and development of artificial intelligence (AI)-assisted methods for image processing, feature extraction, plaque identification and characterization. We performed parallel searches of the medical and technical literature from 1995 to 2021 focusing respectively on human plaque characterization using various imaging modalities and the use of AI-assisted computer aided diagnosis (CAD) to detect and classify atherosclerotic plaques, including their composition and the presence of high-risk features denoting vulnerable plaques. A total of 122 publications were selected for evaluation and the analysis was summarized in terms of data sources, methods—machine versus deep learning—and performance metrics. Trends in AI-assisted plaque characterization are detailed and prospective research challenges discussed. Future directions for the development of accurate and efficient CAD systems to characterize plaque noninvasively using CCTA are proposed.</jats:p

    Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

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    Background and motivation: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. Methods: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. Findings and conclusions: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach

    Automatic Spatiotemporal Analysis of Cardiac Image Series

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    RÉSUMÉ À ce jour, les maladies cardiovasculaires demeurent au premier rang des principales causes de dĂ©cĂšs en AmĂ©rique du Nord. Chez l’adulte et au sein de populations de plus en plus jeunes, la soi-disant Ă©pidĂ©mie d’obĂ©sitĂ© entraĂźnĂ©e par certaines habitudes de vie tels que la mauvaise alimentation, le manque d’exercice et le tabagisme est lourde de consĂ©quences pour les personnes affectĂ©es, mais aussi sur le systĂšme de santĂ©. La principale cause de morbiditĂ© et de mortalitĂ© chez ces patients est l’athĂ©rosclĂ©rose, une accumulation de plaque Ă  l’intĂ©rieur des vaisseaux sanguins Ă  hautes pressions telles que les artĂšres coronaires. Les lĂ©sions athĂ©rosclĂ©rotiques peuvent entraĂźner l’ischĂ©mie en bloquant la circulation sanguine et/ou en provoquant une thrombose. Cela mĂšne souvent Ă  de graves consĂ©quences telles qu’un infarctus. Outre les problĂšmes liĂ©s Ă  la stĂ©nose, les parois artĂ©rielles des rĂ©gions criblĂ©es de plaque augmentent la rigiditĂ© des parois vasculaires, ce qui peut aggraver la condition du patient. Dans la population pĂ©diatrique, la pathologie cardiovasculaire acquise la plus frĂ©quente est la maladie de Kawasaki. Il s’agit d’une vasculite aigĂŒe pouvant affecter l’intĂ©gritĂ© structurale des parois des artĂšres coronaires et mener Ă  la formation d’anĂ©vrismes. Dans certains cas, ceux-ci entravent l’hĂ©modynamie artĂ©rielle en engendrant une perfusion myocardique insuffisante et en activant la formation de thromboses. Le diagnostic de ces deux maladies coronariennes sont traditionnellement effectuĂ©s Ă  l’aide d’angiographies par fluoroscopie. Pendant ces examens paracliniques, plusieurs centaines de projections radiographiques sont acquises en sĂ©ries suite Ă  l’infusion artĂ©rielle d’un agent de contraste. Ces images rĂ©vĂšlent la lumiĂšre des vaisseaux sanguins et la prĂ©sence de lĂ©sions potentiellement pathologiques, s’il y a lieu. Parce que les sĂ©ries acquises contiennent de l’information trĂšs dynamique en termes de mouvement du patient volontaire et involontaire (ex. battements cardiaques, respiration et dĂ©placement d’organes), le clinicien base gĂ©nĂ©ralement son interprĂ©tation sur une seule image angiographique oĂč des mesures gĂ©omĂ©triques sont effectuĂ©es manuellement ou semi-automatiquement par un technicien en radiologie. Bien que l’angiographie par fluoroscopie soit frĂ©quemment utilisĂ© partout dans le monde et souvent considĂ©rĂ© comme l’outil de diagnostic “gold-standard” pour de nombreuses maladies vasculaires, la nature bidimensionnelle de cette modalitĂ© d’imagerie est malheureusement trĂšs limitante en termes de spĂ©cification gĂ©omĂ©trique des diffĂ©rentes rĂ©gions pathologiques. En effet, la structure tridimensionnelle des stĂ©noses et des anĂ©vrismes ne peut pas ĂȘtre pleinement apprĂ©ciĂ©e en 2D car les caractĂ©ristiques observĂ©es varient selon la configuration angulaire de l’imageur. De plus, la prĂ©sence de lĂ©sions affectant les artĂšres coronaires peut ne pas reflĂ©ter la vĂ©ritable santĂ© du myocarde, car des mĂ©canismes compensatoires naturels (ex. vaisseaux----------ABSTRACT Cardiovascular disease continues to be the leading cause of death in North America. In adult and, alarmingly, ever younger populations, the so-called obesity epidemic largely driven by lifestyle factors that include poor diet, lack of exercise and smoking, incurs enormous stresses on the healthcare system. The primary cause of serious morbidity and mortality for these patients is atherosclerosis, the build up of plaque inside high pressure vessels like the coronary arteries. These lesions can lead to ischemic disease and may progress to precarious blood flow blockage or thrombosis, often with infarction or other severe consequences. Besides the stenosis-related outcomes, the arterial walls of plaque-ridden regions manifest increased stiffness, which may exacerbate negative patient prognosis. In pediatric populations, the most prevalent acquired cardiovascular pathology is Kawasaki disease. This acute vasculitis may affect the structural integrity of coronary artery walls and progress to aneurysmal lesions. These can hinder the blood flow’s hemodynamics, leading to inadequate downstream perfusion, and may activate thrombus formation which may lead to precarious prognosis. Diagnosing these two prominent coronary artery diseases is traditionally performed using fluoroscopic angiography. Several hundred serial x-ray projections are acquired during selective arterial infusion of a radiodense contrast agent, which reveals the vessels’ luminal area and possible pathological lesions. The acquired series contain highly dynamic information on voluntary and involuntary patient movement: respiration, organ displacement and heartbeat, for example. Current clinical analysis is largely limited to a single angiographic image where geometrical measures will be performed manually or semi-automatically by a radiological technician. Although widely used around the world and generally considered the gold-standard diagnosis tool for many vascular diseases, the two-dimensional nature of this imaging modality is limiting in terms of specifying the geometry of various pathological regions. Indeed, the 3D structures of stenotic or aneurysmal lesions may not be fully appreciated in 2D because their observable features are dependent on the angular configuration of the imaging gantry. Furthermore, the presence of lesions in the coronary arteries may not reflect the true health of the myocardium, as natural compensatory mechanisms may obviate the need for further intervention. In light of this, cardiac magnetic resonance perfusion imaging is increasingly gaining attention and clinical implementation, as it offers a direct assessment of myocardial tissue viability following infarction or suspected coronary artery disease. This type of modality is plagued, however, by motion similar to that present in fluoroscopic imaging. This issue predisposes clinicians to laborious manual intervention in order to align anatomical structures in sequential perfusion frames, thus hindering automation o

    Non-communicable Diseases, Big Data and Artificial Intelligence

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    This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine
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