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http://dx.doi.org/10.1164/rccm.202309-1681LE | DOI Listing |
BMC Cardiovasc Disord
December 2024
Department of Cardiothoracic Surgery, Maastricht University Medical Centre, Maastricht, Netherlands.
Introduction: Congenital heart disease (CHD) represents the most common group of congenital anomalies, constitutes a significant contributor to the burden of non-communicable diseases, highlighting the critical need for improved risk assessment tools. Artificial intelligence (AI) holds promise in enhancing outcome predictions for congenital cardiac surgery. This study aims to systematically review the utilization of AI in predicting post-operative outcomes in this population.
View Article and Find Full Text PDFOpen Heart
November 2024
William Harvey Research Institute, Queen Mary University of London, London, UK
Rationale: Excessive daytime sleepiness, an important symptom of obstructive sleep apnea (OSA), is commonly quantified using the Epworth Sleepiness Scale score (ESS). Baseline OSA severity measures (ventilatory burden, flow limitation, and hypoxemia) provide insights into OSA pathophysiology and could predict changes in sleepiness (i.e.
View Article and Find Full Text PDFCardiol Young
November 2024
Paediatric Intensive Care Department and Children's heart centre, Children's Health Ireland at Crumlin, Dublin, Republic of Ireland.
Front Cell Infect Microbiol
November 2024
Laboratório de Pesquisa em Virologia, Departamento de Doenças Infecciosas e Parasitárias, Faculdade de Medicina de São José do Rio Preto, São José do Rio Preto, Brazil.
Since its emergence in 2019, coronavirus disease (COVID-19) has spread worldwide and consumed public health resources. However, the world still has to address the burdens of other infectious diseases that continue to thrive. Countries in the tropics and neotropics, including Brazil, are affected by annual, cyclic dengue epidemics.
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