This research was aimed at exploring the diagnostic and screening effect of composite echocardiography based on the artificial intelligence (AI) segmentation algorithm on fetal congenital heart disease (CHD) during pregnancy, so as to reduce the birth rate of newborns with CHD. A total of 204 fetuses with abnormal heart conditions were divided into group II, group C (optimized with the AI algorithm), and group W (not optimized with the AI algorithm). In addition, 9,453 fetuses with normal heart conditions were included in group I. The abnormal distribution of fetal heart and the difference of cardiac Z score between group II and group I were analyzed, and the diagnostic value of group C and group W for CHD was compared. The results showed that the segmentation details of the proposed algorithm were better than those of the convolutional neural network (CNN), and the Dice coefficient, precision, and recall values were higher than those of the CNN. In fetal CHD, the incidence of abnormal ultrasonic manifestations was ventricular septal defect (98/48.04%), abnormal right subclavian artery (29/14.22%), and persistent left superior vena cava (25/12.25%). The diagnostic sensitivity (75.0% vs. 51.5%), specificity (99.6% vs. 99.2%), accuracy (99.0% vs. 98.2%), negative predictive value (88.5% vs. 78.5%), and positive predictive value (99% vs. 57.7%) of echocardiography segmentation in group C were significantly higher than those in group W. To sum up, echocardiography segmented by the AI algorithm could obviously improve the diagnostic efficiency of fetal CHD during gestation. Cardiac ultrasound parameters of children with CHD changed greatly.
To investigate the value and accuracy of prenatal GE-E10 ultrasound Equipment in predicting fetal abnormal development. 160 pregnant women and women who received prenatal ultrasound examination were selected. Before delivery, all pregnant women were examined by conventional two-dimensional and four-dimensional (4D) ultrasound. 18 fetuses with abnormal development were detected by gold standard in 160 pregnant women. Sensitivity and specificity of two-dimensional color ultrasound in diagnosing fetal abnormal development were 78.38% and 82.60%. The sensitivity and specificity of 4D color ultrasound in diagnosing fetal abnormal development were 81.15% and 83.43%. ROC showed that the AUC (0.873) of 4D color ultrasound was higher than that of two-dimensional color ultrasound (0.827). The diagnostic efficiency of 4D ultrasound is greater. The accuracy, specificity and sensitivity of 4D color ultrasound in the diagnosis of fetal abnormal development is high, and it is valuable for prenatal screening of macrosomia and low birth weight.
By exploring different prenatal diagnosis indications of fetal chromosomal abnormalities, it can provide a theoretical basis and reference value for clinical consultation of pregnant women with similar high-risk factors. In this paper, 1800 pregnant women undergoing amniotic fluid aspiration chromosomal examination in the prenatal diagnosis center were selected as the object of this study. Amniocentesis, fetal cell culture, and karyotype analysis were performed on pregnant women who were 14-20 weeks pregnant and had signed an informed consent. After amniocentesis fetal chromosome analysis, the type of fetal chromosomal abnormality was determined, and the detection rate of chromosomal abnormality was statistically described. Chi-square test was used for comparison between groups, P < 0.05. This study shows that the use of ultrasound screening combined with maternal serum indicators is effective in screening fetal structural abnormalities and chromosomal abnormalities in early pregnancy, and significantly improves the detection rate of chromosomal abnormalities. The detection of fetal structural malformations is also very high, but it should be combined with ultrasound screening of mid-to-late pregnancy. The tricuspid regurgitation and umbilical vein a-wave reversal in the soft ultrasound index can be used as predictors of fetal congenital heart disease in early pregnancy.
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