Ultrasonic Elastography Combined with Human Papilloma Virus Detection Based on Intelligent Denoising Algorithm in Diagnosis of Cervical Intraepithelial Neoplasia

Advances in the clinical application of ultrasound elastography in uterine imaging

Changes in tissue stiffness by physiological or pathological factors in tissue structure are identified earlier than their clinical features. Pathological processes such as uterine fibrosis, adenomyosis, endometrial lesions, infertility, and premature birth can manifest as tissue elasticity changes. In clinical settings, elastography techniques based on ultrasonography, optical coherence tomography, and magnetic resonance imaging are widely used for noninvasive measurement of mechanical properties in patients, providing valuable tool and information for diagnosis and treatment. Ultrasound elastography (USE) plays a critical role in obstetrics and gynecology clinical work because of its simplicity, non-invasiveness, and repeatability. This article reviews the recent progress of USE in uterine tumor diagnosis (especially early diagnosis and treatment effect evaluation), prediction of preterm birth, and intrauterine insemination. We believe that USE, especially shear wave elastography, may serve as a potential means to assess tissue stiffness, thereby improving the diagnosis and treatment of adenomyosis, fibroids, endometrial lesions, cervical cancer, and precise management of preterm birth and intrauterine insemination monitoring.

Adoption of Compound Echocardiography under Artificial Intelligence Algorithm in Fetal Congenial Heart Disease Screening during Gestation

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.