[Abstract]Esophageal stricture is a common esophageal lesion in adults and children, and endoscopic dilatation is currently the standard treatment. However, high recurrence rate and frequent dilations have become a major problem in patients. Esophageal stents provide sustained dilation therapy but can lead to serious complications such as displacement, perforation, and bleeding, necessitating removal. Biodegradable stents, with the advantage of both dilation and self-degradation, are promising potential solutions to this problem. Currently, biodegradable materials are mainly categorized into metals and polymers, leading to the development of magnesium alloy esophageal stents and polymer esophageal stents. Among polymer stents, PLLA stents and SX-ELLA stents have been put into clinical application. In recent years, with the advancement of 3D bioprinting technology, the personalized fabrication of biodegradable stents has become feasible. In this paper, we will outline the current research status and progress of biodegradable magnesium alloy stents and polymer stents, introduce the new process of constructing esophageal stents by 3D bioprinting technology, focus on the clinical research of SX-ELLA stents in pediatric and adult patients. We will also analyze the existing problems with biodegradable stents and the directions for future development.
[Abstract]Echocardiography is a vital diagnostic tool for congenital heart disease (CHD), but its interpretation relies heavily on manual labor, is time-consuming and labor-intensive, and suffers from diagnostic accuracy influenced by the skill level of the sonographer. Furthermore, pediatric hearts are small, have rapid heart rates, and exhibit poor patient compliance, further complicating the examination and creating a gap between clinical expectations and reality. In recent years, artificial intelligence (AI) technology, particularly machine learning, have emerged as powerful image analysis tools. They have demonstrated significant effectiveness in assisting echocardiographic detection of pediatric CHD, highlighting their clinical value. This article reviews the progress of AI applications in pediatric CHD echocardiography and identifies the challenges and issues encountered.
