Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: a systematic review and meta-analysis of diagnostic accuracy studies.

Introduction: Pulmonary arterial hypertension (PAH) is a severe cardiovascular condition characterized by pulmonary vascular remodeling, increased resistance to blood flow, and eventual right heart failure. Right heart catheterization (RHC) is the gold standard diagnostic technique, but due to its invasiveness, it poses risks such as vessel and valve injury. In recent years, machine learning (ML) technologies have offered non-invasive alternatives combined with ML for improving the diagnosis of PAH.

Transesophageal echocardiography-guided percutaneous closure of multiple muscular ventricular septal defects with pulmonary hypertension using single device: A case report.

Background: Surgery is typically used to correct challenging ventricular septal defects (VSDs), such as VSD with pulmonary hypertension and multiple defects. In this case report, we would like to highlight the feasibility of multiple defects VSD closure with single device percutaneously using zero-fluoroscopy technique.

Case Presentation: A 7-year-old child was referred with the main symptom of shortness of breath.