A New Method Using Deep Learning to Predict the Response to Cardiac Resynchronization Therapy.

Clinical parameters measured from gated single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) have value in predicting cardiac resynchronization therapy (CRT) patient outcomes, but still show limitations. The purpose of this study is to combine clinical variables, features from electrocardiogram (ECG), and parameters from assessment of cardiac function with polar maps from gated SPECT MPI through deep learning (DL) to predict CRT response. A total of 218 patients who underwent rest-gated SPECT MPI were enrolled in this study.

A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support.

Purpose: Cardiac resynchronization therapy (CRT) has been established as an important therapy for heart failure. Mechanical dyssynchrony has the potential to predict responders to CRT. The aim of this study was to report the development and the validation of machine learning models which integrate ECG, gated SPECT MPI (GMPS), and clinical variables to predict patients' response to CRT.

Feasibility of Left Ventricle Lead Implantation in Cardiac Resynchronization Therapy Guided by Gated SPECT and Ventricular Remodeling.

Background: Cardiac resynchronization therapy (CRT) may benefit patients with advanced heart failure (HF). Abnormal eccentricity index by gated SPECT is related to structural and functional alterations of the left ventricle (LV).

Objective: The aim of this study is to evaluate the feasibility of LV lead implantation guided by phase analysis and its relationship to ventricular remodeling.

Gender Disparity in First and Senior Authorship in Brazilian Cardiology Journals.

Background: Despite the importance of women in clinical research, no assessment has been made of the fraction of women in a leadership positions in the Cardiology journals of the SBC.

Objectives: To assess the fraction of female authors in the International Journal of Cardiovascular Sciences (IJCS) and the Arquivos Brasileiros de Cardiologia (ABC Cardiol) over the last decades.

Methods: We searched the original articles of the ABC Cardiol, from 2000 to 2019, and of the IJCS, from 2010 to 2019.

Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps.

Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images.

Machine Learning Algorithms to Detect Sex in Myocardial Perfusion Imaging.

Myocardial perfusion imaging (MPI) is an essential tool used to diagnose and manage patients with suspected or known coronary artery disease. Additionally, the General Data Protection Regulation (GDPR) represents a milestone about individuals' data security concerns. On the other hand, Machine Learning (ML) has had several applications in the most diverse knowledge areas.

Artificial Intelligence in Cardiology: Concepts, Tools and Challenges - "The Horse is the One Who Runs, You Must Be the Jockey".

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.