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

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...

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Autores principales: Yin, Chuangao, Wang, Song, Pan, Deng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677374/
https://www.ncbi.nlm.nih.gov/pubmed/34925537
http://dx.doi.org/10.1155/2021/2673013
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author Yin, Chuangao
Wang, Song
Pan, Deng
author_facet Yin, Chuangao
Wang, Song
Pan, Deng
author_sort Yin, Chuangao
collection PubMed
description 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.
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spelling pubmed-86773742021-12-17 Computed Tomography Image Characteristics before and after Interventional Treatment of Children's Lymphangioma under Artificial Intelligence Algorithm Yin, Chuangao Wang, Song Pan, Deng Comput Math Methods Med Research Article 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. Hindawi 2021-12-09 /pmc/articles/PMC8677374/ /pubmed/34925537 http://dx.doi.org/10.1155/2021/2673013 Text en Copyright © 2021 Chuangao Yin et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yin, Chuangao
Wang, Song
Pan, Deng
Computed Tomography Image Characteristics before and after Interventional Treatment of Children's Lymphangioma under Artificial Intelligence Algorithm
title Computed Tomography Image Characteristics before and after Interventional Treatment of Children's Lymphangioma under Artificial Intelligence Algorithm
title_full Computed Tomography Image Characteristics before and after Interventional Treatment of Children's Lymphangioma under Artificial Intelligence Algorithm
title_fullStr Computed Tomography Image Characteristics before and after Interventional Treatment of Children's Lymphangioma under Artificial Intelligence Algorithm
title_full_unstemmed Computed Tomography Image Characteristics before and after Interventional Treatment of Children's Lymphangioma under Artificial Intelligence Algorithm
title_short Computed Tomography Image Characteristics before and after Interventional Treatment of Children's Lymphangioma under Artificial Intelligence Algorithm
title_sort computed tomography image characteristics before and after interventional treatment of children's lymphangioma under artificial intelligence algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677374/
https://www.ncbi.nlm.nih.gov/pubmed/34925537
http://dx.doi.org/10.1155/2021/2673013
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