ABSTRACT
Objective: To investigate the ultrasound characteristics and clinical efficacy of coronary arteries before and after immune blocking therapy with gamma globulin in children with Kawasaki disease. Methods: A total of 64 children with Kawasaki disease who were treated in our hospital from January 2018 to October 2021 were selected. All the children were given immune blocking therapy with gamma globulin on the basis of conventional treatment. The disappearance time of related symptoms and signs (fever, mucosal congestion, cervical lymphadenopathy, and swelling of the hands and feet) in children were counted. The white blood cell count (WBC), platelet count (PLT), C-reactive protein (CRP), and procalcitonin (PCT) levels of the children before and after treatment were compared, and the characteristics of coronary echocardiography before and after treatment were observed for analysis and discussion, to carefully observe whether the coronary artery involvement of the children was improved. Results: The inner diameter of the left and right coronary arteries significantly decreased (P < 0.05), and the levels of leukocytes, platelets, CRP, erythrocyte sedimentation rate, vascular endothelial growth factor (VEGF), and endostatin were significantly decreased compared with those before treatment, with a statistical difference (P < 0.05). Conclusion: The effect of gamma globulin in the treatment of Kawasaki disease is remarkable, which can improve the blood indexes, VEGF, and endostatin levels in children, significantly reduce coronary dilatation, and reduce the incidence of coronary artery disease. Echocardiography is of high value in the examination of children with Kawasaki disease, which can accurately detect the size, location, and inner diameter of coronary artery lesions, and can effectively evaluate the treatment effect on children.
ABSTRACT
Objective Traditional breast ultrasound relies too much on the operation skills of diagnostic doctors, and the repeatability in different doctors was low. This study aimed to evaluate the assistant diagnostic value of S-Detect artificial intelligence (AI) system in differentiating benign from malignant breast masses. Methods The ultrasound images of 40 patients who underwent ultrasound examination in our hospital were collected. The conventional ultrasound images, elastic images, and S-Detect mode of breast lesions were analyzed. The breast imaging reporting and data system recommended by the American Society of Radiology (BI-RADS) classification for each breast mass was evaluated both by the doctor and AI. The receiver operator characteristics (ROC) curves were drawn to compare the diagnostic efficiency. Result Among the 40 lesions, 16 were benign, and 24 were malignant. The S-Detect AI system had a high diagnostic efficiency for malignant mass, with sensitivity, specificity, and accuracy of 95.8%, 93.8%, and 89.6%. The accuracy of AI was higher than the elastic image and then than the conventional gray-scale image. With the assistance of the S-Detect AI system, the accuracy of BI-RADS classification was improved significantly. Conclusion The S-Detect AI system will enhance breast cancer diagnostic accuracy and improve ultrasound examination quality.
