ABSTRACT
Background: Heart failure remains a considerable burden to healthcare in Asia. Early intervention, mainly using echocardiography, to assess cardiac function is crucial. However, due to limited resources and time, the procedure has become more challenging during the COVID-19 pandemic. On the other hand, studies have shown that artificial intelligence (AI) is highly potential in complementing the work of clinicians to diagnose heart failure accurately and rapidly. Methods: We systematically searched Europe PMC, ProQuest, Science Direct, PubMed, and IEEE following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and our inclusion and exclusion criteria. The 14 selected works of literature were then assessed for their quality and risk of bias using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies). Results: A total of 2105 studies were retrieved, and 14 were included in the analysis. Five studies posed risks of bias. Nearly all studies included datasets in the form of 3D (three dimensional) or 2D (two dimensional) images, along with apical four-chamber (A4C) and apical two-chamber (A2C) being the most common echocardiography views used. The machine learning algorithm for each study differs, with the convolutional neural network as the most common method used. The accuracy varies from 57% to 99.3%. Conclusions: To conclude, current evidence suggests that the application of AI leads to a better and faster diagnosis of left heart failure through echocardiography. However, the presence of clinicians is still irreplaceable during diagnostic processes and overall clinical care;thus, AI only serves as complementary assistance for clinicians.
ABSTRACT
Background Children are susceptible to SARS-CoV-2 infection and often present mild manifestations. However, severe and critical cases have also been reported. The inflammation and coagulation marker profile pattern in these patients along with the white blood cell differential count in critical PICU cases with non-COVID-19 etiology is not entirely clear.Objective To evaluate the inflammation and coagulation profiles in children presenting with severe/critical SARS-CoV-2 infection. Methods A systematic search and review of scientific literature was conducted following the PRISMA guidelines using ProQuest, SCOPUS, EBSCOHost, ScienceDirect, Cochrane, EMBASE, and Pubmed databases. All relevant original studies until March 11, 2021, were included. The risk of bias was appraised using the Modified Newcastle Ottawa Scale and JBI Critical Appraisal Checklist tools. Results We identified 14 studies across 6 countries, including a total sample of 159 severe and critically ill pediatric COVID-19 patients. Most of the subjects showed normal leukocytes, but in-creased CRP, procalcitonin, ferritin, and IL-6. Studies on coagula-tion profiles showed normal platelets, PT, aPTT, and inconsistent D-dimer results. Conclusion Inflammation and coagulation parameters in severe/ critically ill children with COVID-19 are atypical. Several inflammatory markers were elevated, including CRP, ferritin, procalcitonin, and IL-6. However, the elevated marker values are still lower compared to non-COVID infection patients. Further investigation of the parameters need to be done in serial examina-tion multicenter studies, which include control subjects. [Paediatr Indones. 2022;62:411-21;DOI: https://doi.org/10.14238/ pi62.6.2022.411-21 ].