Echocardiographic assessment of pediatric heart transplantation: A single-center experience in China.

BACKGROUND: Pediatric heart transplant (HT) has become the standard of care for end-stage heart failure in children worldwide. Serial echocardiographic evaluations of graft anatomy and function during follow-up are crucial for post-HT management. However, evolution of cardiac structure and function after pediatric HT has not been well described, especially during first year post-HT. This study aimed to characterize the evolution of cardiac structure and function after pediatric HT and investigate the correlation between biventricular function with adverse clinical outcomes. METHODS: A single-center retrospective study of echocardiographic data obtained among 99 pediatric HT patients was conducted. Comprehensive echocardiographic examination was performed in all patients at 1-, 3-, 6-, 9- and 12-months post-HT. We obtained structural, functional and hemodynamic parameters from both left- and right-side heart, such as left ventricular stroke volume (LVSV), left ventricular ejection fraction (LVEF), right ventricular fractional area change (RVFAC), etc. The cardiac evolution of pediatric HT patients during first post-HT year was described and compared between different time points. We also explored the correlation between cardiac function and major adverse transplant events (MATEs). RESULTS: 1) Evolution of left heart parameters: left atrial length, mitral E velocity, E/A ratio, LVSV and LVEF significantly increased while mitral A velocity significantly decreased over the first year after HT (P < .05). Compared with 1 month after HT, interventricular septum (IVS) and left ventricular posterior wall (LVPW) decreased at 3 months but increased afterwards. (2) Evolution of right heart parameters: right ventricular base diameter and mid-diameter; right ventricular length diameter, tricuspid E velocity, E/A ratio, tricuspid annular velocity e' at free wall, and RVFAC increased, while tricuspid A velocity decreased over the first year after HT (P < .05). (3) Univariate logistic regression model suggests that biventricular function parameters at 1-year post-HT (LVEF, RVFAC, tricuspid annular plane systolic excursion and tricuspid lateral annular systolic velocity) were associated with MATEs. CONCLUSION: Gradual improvement of LV and RV function was seen in pediatric HT patients within the first year. Biventricular function parameters associated with MATEs. The results of this study pave way for designing larger and longer follow-up of this population, potentially aiming at using multiparameter echocardiographic prediction of adverse events.

Advances in the Application of Artificial Intelligence in Fetal Echocardiography.

Congenital heart disease is a severe health risk for newborns. Early detection of abnormalities in fetal cardiac structure and function during pregnancy can help patients seek timely diagnostic and therapeutic advice, and early intervention planning can significantly improve fetal survival rates. Echocardiography is one of the most accessible and widely used diagnostic tools in the diagnosis of fetal congenital heart disease. However, traditional fetal echocardiography has limitations due to fetal, maternal, and ultrasound equipment factors and is highly dependent on the skill level of the operator. Artificial intelligence (AI) technology, with its rapid development utilizing advanced computer algorithms, has great potential to empower sonographers in time-saving and accurate diagnosis and to bridge the skill gap in different regions. In recent years, AI-assisted fetal echocardiography has been successfully applied to a wide range of ultrasound diagnoses. This review systematically reviews the applications of AI in the field of fetal echocardiography over the years in terms of image processing, biometrics, and disease diagnosis and provides an outlook for future research.

Application and Progress of Artificial Intelligence in Fetal Ultrasound.

Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician's workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.