Artificial Intelligence/trends

ABC

Editorial

Ventricular Repolarization as a Tool to Monitor Electrical Activity of the Heart

Acute pulmonary embolism has a heterogeneous clinical presentation and the development of clinical tools for better stratification of the patients prognosis is extremely desired. Being able to discern which patient is most likely to have complications or even death is critical in the age of precision medicine. This may assist in choosing a more intensive and individualized treatment.

Computed tomography pulmonary angiography (CTPA) is already the most used method in emergencies for the diagnosis and stratification of patients with APE. The great variability of clinical presentations, whether in patients with cancer or different thrombogenic conditions, makes some quantitative data more attractive than just the qualitative analysis of the exam.^–

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ABC

Review Article

Artificial Intelligence in Cardiology: Concepts, Tools and Challenges – “The Horse is the One Who Runs, You Must Be the Jockey”

Erito Marques de Souza Filho et al. Fernando de Amorim Fernandes Celine Lacerda de Abreu Soares Flavio Luiz Seixas … Alair Augusto Sarmet M.D. dos Santos Ronaldo Altenburg Gismondi Evandro Tinoco Mesquita Claudio Tinoco Mesquita

Abstract

The recent advances at hardware level and the increasing requirement of personalization of care associated with the urgent needs of value creation for the patients has helped Artificial Intelligence (AI) to promote a significant paradigm shift in the most diverse areas of medical knowledge, particularly in Cardiology, for its ability to support decision-making and improve diagnostic and prognostic performance. In this context, the present work does a non-systematic review of the main papers published on AI in Cardiology, focusing on its main applications, potential impacts and challenges.

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IJCS

Editorial

Artificial Intelligence and Machine Learning in Cardiology – A Change of Paradigm

Claudio Tinoco Mesquita

We are experiencing a change paradigm in modern life. With the presence of computers and intelligent machines everywhere, the predictions of science-fiction books from years ago gradually become reality; these are the times of pervasive computing. Among the computational most frequently mentioned tools in clinical studies and seen with enthusiasm by the scientific community is the Artificial Intelligence and consequently the machines that learn, which is best quoted in its original English form, Machine Learning. In general Artificial Intelligence is defined as the constellation of items (algorithms, robotics, neural networks) that allow a software to have intelligence properties that are comparable to those of a human being, among them learning from databases with minimal human interference.

Obermeyer and Emanuel recently wrote an editorial stating that Machine Learning has become widespread and imperative for solving complex problems in the various fields of science, and in the medical field its use will transform the practice. The use of artificial intelligence is evolving increasingly in cardiology and there are already excellent examples in several areas. Using a sophisticated learning system to electrocardiographic interpretation, Li et al. achieved that electrocardiographic patterns were automatically recognized with an accuracy of 88% for the classification of abnormal rhythms⁽⁾. One of the most important limitations of the system studied was the quality of the electrocardiographic signal for interpretation and learning, which highlights one of the essential characteristics of Machine Learning, that is the need for accurate and reproducible information for the formation of databases.^, As databases are usually produced from patients selected for their basic condition, one of the most important points for development is the creation of broader and generalizable databases that do not induce biases in the interpretation of findings, point in which industry is heavily investing at the moment.

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Ventricular Repolarization as a Tool to Monitor Electrical Activity of the Heart

Artificial Intelligence in Cardiology: Concepts, Tools and Challenges – “The Horse is the One Who Runs, You Must Be the Jockey”

Abstract

Artificial Intelligence and Machine Learning in Cardiology – A Change of Paradigm

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Electronic Document Format (Vancouver)

Electronic Document Format (ABNT)

Electronic Document Format (APA)

Electronic Document Format (ISO)

Electronic Document Format (Vancouver)

Electronic Document Format (ABNT)

Electronic Document Format (APA)

Electronic Document Format (ISO)