Artificial intelligence and the cardiologist: what you need to know for 2020

Marvao, Antonio de; Dawes, Timothy J W; Howard, James P.; O’Regan, Declan P

doi:10.1136/heartjnl-2019-316033

Cited by 40 publications

(28 citation statements)

References 8 publications

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“…There is growing interest in the interface between advanced cardiac imaging with artificial intelligence and machine learning [60]. Such approaches have already been developed and validated for CMR image segmentation, motion and deformation analysis.…”

Section: Future Horizonsmentioning

confidence: 99%

Sudden Cardiac Death Prediction in Non-ischemic Dilated Cardiomyopathy: a Multiparametric and Dynamic Approach

Hammersley

Halliday

2020

Curr Cardiol Rep

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Purpose of Review Sudden cardiac death is recognised as a devastating consequence of non-ischaemic dilated cardiomyopathy. Although implantable cardiac defibrillators offer protection against some forms of sudden death, the identification of patients in this population most likely to benefit from this therapy remains challenging and controversial. In this review, we evaluate current guidelines and explore established and novel predictors of sudden cardiac death in patients with non-ischaemic dilated cardiomyopathy. Recent Findings Current international guidelines for primary prevention implantable defibrillator therapy do not result in improved longevity for many patients with non-ischemic cardiomyopathy and severe left ventricular dysfunction. More precise methods for identifying higher-risk patients that derive true prognostic benefit from this therapy are required. Summary Dynamic and multi-parametric characterization of myocardial, electrical, serological and genetic substrate offers novel strategies for predicting major arrhythmic risk. Balancing the risk of non-sudden death offers an opportunity to personalize therapy and avoid unnecessary device implantation for those less likely to derive benefit.

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Section: Future Horizonsmentioning

confidence: 99%

Sudden Cardiac Death Prediction in Non-ischemic Dilated Cardiomyopathy: a Multiparametric and Dynamic Approach

Hammersley

Halliday

2020

Curr Cardiol Rep

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“…Além disso, o DL, que é o extremo da interação e adaptação de aprendizado, pode estabelecer conexões neurais e diversidade para integrar diferentes bases de dados. 11 A IA, a ML e o DL já apresentam aplicações em estudo para IC, seja como diagnóstico, avaliação de prognóstico, TMO ou ainda para selecionar pacientes com maior benefício para diversas terapias. Isso pode ser feito, por exemplo, na distinção de fenótipos, alocando pacientes em diferentes perfis de assinatura de doença; 12 na melhor acurácia para o diagnóstico de IC aguda em relação ao médico; 13 e no eventual direcionamento para terapias novas ou já estabelecidas, como análise adicional do ECG basal para identificar paciente melhor respondedor à TRC.…”

Section: Palavras-chaveunclassified

“…Além disso, o DL, que é o extremo da interação e adaptação de aprendizado, pode estabelecer conexões neurais e diversidade para integrar diferentes bases de dados. 11 …”

Section: Inteligência Artificial E Big Data Na Icunclassified

Tópicos Emergentes em Insuficiência Cardíaca: O Futuro na Insuficiência Cardíaca: Telemonitoramento, Wearables , Inteligência Artificial e Ensino na Era Pós-Pandemia

Freitas

Silveira²,

Conceição-Souza

et al. 2020

Arquivos Brasileiros De Cardiologia

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“…For example, automated guided vehicles (AGV) have been broadly applied to the handling of heavy objects or large devices in production environments [3], multi-axis CNC machines have been extensively used to machine parts with sculptured surfaces in the fields of machinofacture and aerospace [4], robots have been developed for welding, spraying and assembling in smart factories [5], firefighting robots have come into service in order to reduce the risks that human firefighters face [6], the Roomba promises its customers "effortless cleaning" or robot vacuums allow owners to "clean your floors without lifting a finger" [7], and feeding assistive robots have been proposed to help older people and people with disabilities in their daily life [8]. Thanks to the development of artificial intelligence [9], machine vision [10], sensor technology [11], etc., autonomous driving technology [12], as a new automation technology which is deeply integrated with various advanced technologies [13], is witnessed to have leapt forward in the last decade [14], and as a result, many unicorn enterprises are known to have developed, such as Waymo [15], Momenta [16], and Apollo [17].…”

Section: Introductionmentioning

confidence: 99%

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Zeng

Liu

et al. 2020

Sensors

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How to make a controller robust and stable to reject the disturbance of uncertainty is an inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road sweeper, a heading-error-based first order linear active disturbance rejective controller (HFO-LADRC) is proposed in this paper. To eliminate the lateral error and the heading error at the same time, a new model, called the heading-error-based model, is proposed for lateral motion, and the Lyapunov function was employed to explore the convergence ability of the heading error and lateral error. Since the heading-error-based model is first order, the ADRC is designed as first order and linear, and each module of the HFO-LADRC has been devised in detail. To ensure solution accuracy, the fourth order Runge–Kutta method was adopted as the differential system solver, and a typical ring scenario and a double lane-changing scenario were designed referencing the standard. Considering the obvious influence, wheelbase uncertainty, steering ratio uncertainty and Gaussian white noise disturbance were taken into account for the tests. The results illustrate that, in the case of both wheelbase uncertainty and steer ratio uncertainty, the HFO-LADRC has strong robustness and stability compared with a typical pure pursuit controller and classical SO-LADRC.

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Artificial intelligence and the cardiologist: what you need to know for 2020

Cited by 40 publications

References 8 publications

Sudden Cardiac Death Prediction in Non-ischemic Dilated Cardiomyopathy: a Multiparametric and Dynamic Approach

Sudden Cardiac Death Prediction in Non-ischemic Dilated Cardiomyopathy: a Multiparametric and Dynamic Approach

Tópicos Emergentes em Insuficiência Cardíaca: O Futuro na Insuficiência Cardíaca: Telemonitoramento, Wearables , Inteligência Artificial e Ensino na Era Pós-Pandemia

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

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