Computed Tomography Image Characteristics before and after Interventional Treatment of Children’s Lymphangioma under Artificial Intelligence Algorithm

• Published in: Computational and mathematical methods in medicine Vol. 2021; pp. 1 - 8
• Main Authors: Yin, Chuangao, Wang, Song, Pan, Deng
• Format: Journal Article
• Language: English
• Published: United States Hindawi 09.12.2021
• Subjects: Algorithms; Antineoplastic Agents - administration & dosage; Antineoplastic Agents - therapeutic use; Artificial Intelligence; Bleomycin - administration & dosage; Bleomycin - therapeutic use; Child; Child, Preschool; Computational Biology; Female; Humans; Infant; Lymphangioma - diagnostic imaging; Lymphangioma - drug therapy; Male; Neural Networks, Computer; Retrospective Studies; Tomography, X-Ray Computed - methods; Tomography, X-Ray Computed - statistics & numerical data; Ultrasonography, Interventional - methods; Ultrasonography, Interventional - statistics & numerical data
• DOI: 10.1155/2021/2673013
• ISSN: 1748-670X; 1748-6718

The artificial intelligence algorithm was used to analyze the characteristics of computed tomography (CT) images before and after interventional treatment of children’s lymphangioma. Retrospective analysis was performed, and 30 children with lymphangioma from the hospital were recruited as the study subjects. The ultrasound-guided bleomycin interventional therapy was adopted and applied to CT scanning through convolutional neural network (CNN). The CT imaging-related indicators before and after interventional therapy were detected, and feature analysis was performed. In addition, the CNN algorithm was adopted to segment the image of the tumor was clearer and more accurate. At the same time, the Dice similarity coefficient (DSC) of the CNN algorithm was 0.9, which had a higher degree of agreement. In terms of clinical symptoms, the cured children’s lesions disappeared, the skin surface returned to normal color, and the treatment was smooth. In the two cases with effective treatment, the cystic mass at the lesion site was significantly smaller, and the nodules disappeared. CT images before interventional therapy showed that lymphangiomas in children were more common in the neck. The cystic masses at all lesion sites varied in diameter and size, and most of them were similar to round and irregular, with uniform density distribution. The boundary was clear, the cyst was solid, and there were different degrees of compression and spread to the surrounding structure. Most of them were polycystic, and a few of them were single cystic. After interventional treatment, CT images showed that 27 cases of cured children’s lymphangioma completely disappeared. Lymphangioma was significantly reduced in two children with effective treatment. Edema around the tumor also decreased significantly. Patients who did not respond to the treatment received interventional treatment again, and the tumors disappeared completely on CT imaging. No recurrence or new occurrence was found in three-month follow-up. The total effective rate of interventional therapy for lymphangioma in children was 96.67%. The CNN algorithm can effectively compare the CT image features before and after interventional treatment for children’s lymphangioma. It was suggested that the artificial intelligence algorithm-aided CT imaging examination was helpful to guide physicians in the accurate treatment of children’s lymphangioma.