Os recentes avanços ao nível de hardware e a crescente exigência de personalização dos cuidados associados às necessidades urgentes de criação de valor para os pacientes contribuíram para que a Inteligência Artificial (IA) promovesse uma mudança significativa de paradigma nas mais diversas áreas do conhecimento médico, em particular em Cardiologia, por sua capacidade de apoiar a tomada de decisões e melhorar o desempenho diagnóstico e prognóstico. Nesse contexto, o presente trabalho faz uma revisão não-sistemática dos principais trabalhos publicados sobre IA em Cardiologia, com foco em suas principais aplicações, possíveis impactos e desafios.

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. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.

Introdução Em um futuro não muito distante, um programa de computador artificialmente inteligente provavelmente diagnosticará as doenças cardíacas com mais precisão que um cardiologista certificado. O conhecimento biomédico cresce significativamente, impossibilitando que os profissionais de saúde contemporâneos se mantenham atualizados sobre todos os conteúdos publicados em seu campo. Da mesma forma, informações sobre os pacientes estão cada vez mais numerosas e acessíveis, tornando impraticável o gerenciamento, filtragem e seleção em tempo real. Nesse contexto, a Inteligência Artificial (IA) tem um [...] Introdução Em um futuro não muito distante, um programa de computador artificialmente inteligente provavelmente diagnosticará as doenças cardíacas com mais precisão que um cardiologista certificado. O conhecimento biomédico cresce significativamente, impossibilitando que os profissionais de saúde contemporâneos se mantenham atualizados sobre todos os conteúdos publicados em seu campo. Da mesma forma, informações sobre os pacientes estão cada vez mais numerosas e acessíveis, tornando impraticável o gerenciamento, filtragem e seleção em tempo real. Nesse contexto, a Inteligência Artificial (IA) tem um

Background: In many cities around the world, the mortality rate from cancer (CA) has exceeded that from disease of the circulatory system (DCS). Objectives: To compare the mortality curves from DCS and CA in the most populous capital cities of the five regions of Brazil. Methods: Data of mortality rates from DCS and CA between 2000 and 2015 were collected from the Mortality Information System of Manaus, Salvador, Goiania, Sao Paulo and Curitiba, and categorized by age range into early (30-69 years) and late (>= 70 years), and by gender of the individuals. Chapters II and IX of the International Classification of Diseases-10 were used for the analysis of causes of deaths. The Joinpoint regression model was used to assess the tendency of the estimated annual percentage change of mortality rate, and the Monte Carlo permutation test was used to detect when changes occurred. Statistical significance was set at 5%. Results: There was a consistent decrease in early and late mortality from DCS in both genders in the cities studied, except for late mortality in men in Manaus. There was a tendency of decrease of mortality rates from CA in Sao Paulo and Curitiba, and of increase in the rates from CA in Goiania. In Salvador, there was a decrease in early mortality from CA in men and women and an increase in late mortality in both genders. Conclusion: There was a progressive and marked decrease in the mortality rate from DCS and a maintenance or slight increase in CA mortality in the five capital cities studied. These phenomena may lead to the intersection of the curves, with predominance of mortality from CA (old and new cases).

Variables that influence left ventricular mechanical dyssynchrony measurements by