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Objective: The paper aimed to improve the accuracy limitations of traditional two-dimensional ultrasound and surgical procedures in the diagnosis and management of congenital heart disease (chd), and to improve the diagnostic and therapeutic level of chd. Method: This article first collected patient data through real-time imaging and body surface probes, and then diagnosed 150 patients using three-dimensional echocardiography. In order to verify the effectiveness of the combination therapy, 60 confirmed patients were divided into a control group and an experimental group. The control group received conventional two-dimensional ultrasound and surgical treatment, while the experimental group received three-dimensional ultrasound and image guided surgical treatment. Result: In the second diagnosis, the diagnostic accuracy of type 1, type 2, and type 3 in the control group was 84.21%, 84.02%, and 83.38%, respectively. The diagnostic accuracy rates of type 1, type 2, and type 3 in the experimental group were 92.73%, 92.82%, and 92.83%, respectively. In the control group, 2 males and 1 female experienced heart failure after surgery. However, in the experimental group, 0 males and 0 females experienced heart failure after surgery. Conclusion: The combination of three-dimensional echocardiography and image guided surgery can improve diagnostic accuracy and surgical treatment effectiveness, thereby reducing risks and complications, and improving surgical success rate. [ABSTRACT FROM AUTHOR]

Background: Prenatal ultrasound is the preferred modality for diagnosing fetal congenital heart disease. Given issues of physician proficiency and hospital distribution, we propose a dynamic sequential cross-sectional scanning (SCS) to explore the feasibility of cardiac screening by sonographers with less than 5 years of experience in ultrasound. Materials and methods: Twenty residents were randomly divided into two groups, receiving training in the American Institute of Ultrasound in Medicine (AIUM) fetal echocardiography and the SCS method. According to the needs of training, the professional staff developed the theoretical knowledge question bank, the CHD ultrasonic video disease bank, and the assessment scale. Trainees completed the pre-training examination, theory and skill operation training, and post-training assessment. For the two groups, the theoretical knowledge, skill operation and disease diagnosis were analyzed statistically before and after training. Results: After training, the trainees in both groups had significantly improved knowledge and diagnostic abilities, their diagnostic thinking about CHD was clear, and they could identify major or even all structural abnormalities and make a definite diagnosis. In terms of skill operation, both groups could complete all required scanning within the specified time. The scanning time of the SCS group was significantly lower than that of the AIUM group, and the effect of the receptor site in the AIUM group was significantly higher than that in the SCS group. Conclusion: SCS can be used as a new rapid fetal cardiac scanning method and try to popularize among echocardiographer. [ABSTRACT FROM AUTHOR]

Background: Congenital heart disease (CHD) is one of the most common birth defects in the world. It is the leading cause of infant mortality, necessitating an early diagnosis for timely intervention. Prenatal screening using ultrasound is the primary method for CHD detection. However, its effectiveness is heavily reliant on the expertise of physicians, leading to subjective interpretations and potential underdiagnosis. Therefore, a method for automatic analysis of fetal cardiac ultrasound images is highly desired to assist an objective and effective CHD diagnosis. Method: In this study, we propose a deep learning-based framework for the identification and segmentation of the three vessels—the pulmonary artery, aorta, and superior vena cava—in the ultrasound three vessel view (3VV) of the fetal heart. In the first stage of the framework, the object detection model Yolov5 is employed to identify the three vessels and localize the Region of Interest (ROI) within the original full-sized ultrasound images. Subsequently, a modified Deeplabv3 equipped with our novel AMFF (Attentional Multi-scale Feature Fusion) module is applied in the second stage to segment the three vessels within the cropped ROI images. Results: We evaluated our method with a dataset consisting of 511 fetal heart 3VV images. Compared to existing models, our framework exhibits superior performance in the segmentation of all the three vessels, demonstrating the Dice coefficients of 85.55%, 89.12%, and 77.54% for PA, Ao and SVC respectively. Conclusions: Our experimental results show that our proposed framework can automatically and accurately detect and segment the three vessels in fetal heart 3VV images. This method has the potential to assist sonographers in enhancing the precision of vessel assessment during fetal heart examinations. [ABSTRACT FROM AUTHOR]

Background: Prenatal detection of critical congenital heart disease (CCHD) optimises perinatal decision-making and neonatal outcomes. The objective of this study was to determine the prenatal screening performance, care pathways and perinatal outcomes for prenatally and postnatally diagnosed cases of CCHD over a four-year period. Study design: This retrospective cohort study in a tertiary centre and its two affiliated secondary sites examined all cases of CCHD, including cases of pregnancy termination and in-utero fetal death, neonatal death and liveborn babies that underwent cardiac catheterization or surgery in the first six weeks of life. Prenatal and postnatal data were ascertained from the first trimester assessment for all patients diagnosed prenatally. Cases requiring intervention that were first identified in the postnatal period were included to determine prenatal detection rates. Follow-up for all cases of CCHD continued to one year of age. Results: In a consecutive cohort of 49,950 pregnancies in a 4-year period 01/2019 to 12/2022, a prenatal diagnosis of CCHD was made in 96 cases, yielding a prevalence of 1.9 per 1000 births. The prenatal detection for right duct-dependant heart pathology and congenital heart block was 100%, 85% for left duct-dependant pathology and 93% for transposition of the great arteries (TGA). In the prenatally diagnosed group, 37% of cases were complicated by extracardiac structural abnormalities, a genetic diagnosis or both. All cases of prenatal detection were identified in the context of routine anatomy screening rather than specialist Fetal Cardiac screening services. Almost half of all pregnancies complicated by CCHD did not undergo neonatal cardiac intervention, by virtue of parental choice determined either prenatally or after birth. An additional eight babies were diagnosed with CCHD in the neonatal period, such that the prenatal detection rate for CCHD was 92% (96/104, 95% CI = 84%-96%). Survival at 1-year for infants deemed suitable for CCHD surgery was 85%. Conclusion: In a large unselected population, optimal rates of prenatal detection of critical congenital heart disease can be achieved by a protocolised approach to mid-trimester fetal anatomy ultrasound, underpinned by a programme of sonographer education and training. The cardiac abnormalities most likely to evade prenatal detection are left-sided obstructive lesions. [ABSTRACT FROM AUTHOR]