Abdominal Enhanced Computed Tomography Image by Artificial Intelligence Algorithm in the Diagnosis of Abdominal Aortic Aneurysm
The purpose of this study was to investigate the clinical value of CT angiography (CTA) images processed by the segmentation denoising technique based on deep convolution neural network algorithm in the diagnosis of abdominal aortic aneurysm (AAA) and the detection of disease changes. A total of 98...
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| Published in: | Scientific programming Vol. 2021; pp. 1 - 8 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Hindawi
28.12.2021
John Wiley & Sons, Inc |
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| ISSN: | 1058-9244, 1875-919X |
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| Abstract | The purpose of this study was to investigate the clinical value of CT angiography (CTA) images processed by the segmentation denoising technique based on deep convolution neural network algorithm in the diagnosis of abdominal aortic aneurysm (AAA) and the detection of disease changes. A total of 98 patients with ruptured AAA were retrospectively selected as the study subjects. Patients were grouped according to whether the CTA images were optimized, the images receiving artificial intelligence segmentation and denoising were set as the observation group, and the CTA images without optimization were set as the control group. The detection and diagnosis effects of CTA images before and after the treatment were compared. The surgical results were used as the standard to analyze the diagnostic effect, and the maximum diameter measurement results of AAA and the proportion results of intraluminal thrombus (ILT) were compared. Although the sensitivity and accuracy of diagnosis in the observation group (97.73% and 94.9%) were higher than those in the control group (95.45% and 92.86%), there was no significant statistical significance (P>0.05). When the diameter of AAA was no less than 5 cm, all results showed that the coverage percentage of intraluminal thrombus (ILT) was over 50%. When the diameter of AAA was less than 5 cm, only 55.56% of the results showed that the percentage of ILT coverage was over 50%, with considerable differences (P>0.05). According to the results of the study, it was found that there was a certain relationship between the thrombus coverage of the abdominal aortic wall and the growth rate of AAA. The deep convolution neural network algorithm had a certain effect on the treatment of CTA, but it is not obvious. However, CTA had a better clinical diagnostic effect on AAA. |
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| AbstractList | The purpose of this study was to investigate the clinical value of CT angiography (CTA) images processed by the segmentation denoising technique based on deep convolution neural network algorithm in the diagnosis of abdominal aortic aneurysm (AAA) and the detection of disease changes. A total of 98 patients with ruptured AAA were retrospectively selected as the study subjects. Patients were grouped according to whether the CTA images were optimized, the images receiving artificial intelligence segmentation and denoising were set as the observation group, and the CTA images without optimization were set as the control group. The detection and diagnosis effects of CTA images before and after the treatment were compared. The surgical results were used as the standard to analyze the diagnostic effect, and the maximum diameter measurement results of AAA and the proportion results of intraluminal thrombus (ILT) were compared. Although the sensitivity and accuracy of diagnosis in the observation group (97.73% and 94.9%) were higher than those in the control group (95.45% and 92.86%), there was no significant statistical significance (P>0.05). When the diameter of AAA was no less than 5 cm, all results showed that the coverage percentage of intraluminal thrombus (ILT) was over 50%. When the diameter of AAA was less than 5 cm, only 55.56% of the results showed that the percentage of ILT coverage was over 50%, with considerable differences (P>0.05). According to the results of the study, it was found that there was a certain relationship between the thrombus coverage of the abdominal aortic wall and the growth rate of AAA. The deep convolution neural network algorithm had a certain effect on the treatment of CTA, but it is not obvious. However, CTA had a better clinical diagnostic effect on AAA. The purpose of this study was to investigate the clinical value of CT angiography (CTA) images processed by the segmentation denoising technique based on deep convolution neural network algorithm in the diagnosis of abdominal aortic aneurysm (AAA) and the detection of disease changes. A total of 98 patients with ruptured AAA were retrospectively selected as the study subjects. Patients were grouped according to whether the CTA images were optimized, the images receiving artificial intelligence segmentation and denoising were set as the observation group, and the CTA images without optimization were set as the control group. The detection and diagnosis effects of CTA images before and after the treatment were compared. The surgical results were used as the standard to analyze the diagnostic effect, and the maximum diameter measurement results of AAA and the proportion results of intraluminal thrombus (ILT) were compared. Although the sensitivity and accuracy of diagnosis in the observation group (97.73% and 94.9%) were higher than those in the control group (95.45% and 92.86%), there was no significant statistical significance ( P > 0.05 ). When the diameter of AAA was no less than 5 cm, all results showed that the coverage percentage of intraluminal thrombus (ILT) was over 50%. When the diameter of AAA was less than 5 cm, only 55.56% of the results showed that the percentage of ILT coverage was over 50%, with considerable differences ( P > 0.05 ). According to the results of the study, it was found that there was a certain relationship between the thrombus coverage of the abdominal aortic wall and the growth rate of AAA. The deep convolution neural network algorithm had a certain effect on the treatment of CTA, but it is not obvious. However, CTA had a better clinical diagnostic effect on AAA. |
| Author | Zhou, Qingyun Wang, Qinning Zheng, Tao Shao, Guofeng Ye, Mengmeng |
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| CitedBy_id | crossref_primary_10_32604_cmc_2023_038059 crossref_primary_10_32604_cmes_2022_018948 crossref_primary_10_1016_j_nexres_2024_100059 crossref_primary_10_3389_fpsyg_2022_877737 |
| Cites_doi | 10.2214/AJR.08.1593 10.18632/aging.101702 10.1016/j.imavis.2020.103975 10.1007/978-3-030-33128-3_1 10.1093/neuros/nyw113 10.1007/s00261-017-1450-7 10.1371/journal.pone.0202672 10.1007/s10278-019-00227-x 10.18926/AMO/57710 10.1115/1.4023437 10.1148/radiol.2020191723 10.1586/14779072.2015.1074861 10.1016/s1078-5884(96)80104-3 10.1007/s10916-018-1088-1 10.1016/j.jdent.2018.07.015 10.1007/s00261-007-9268-3 10.1016/j.avsg.2018.10.018 10.1155/2021/7388825 10.1002/mp.13658 10.3233/THC-202657 10.1038/nrcardio.2010.180 10.21470/1678-9741-2016-0050 10.1016/j.ejvs.2010.03.032 10.1109/TMI.2004.828354 10.1007/s00423-016-1401-8 10.23736/S0021-9509.18.10380-6 |
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| Copyright | Copyright © 2021 Tao Zheng et al. Copyright © 2021 Tao Zheng et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Abdomen Algorithms Angiography Aorta Aortic aneurysms Artificial intelligence Artificial neural networks Computed tomography Deep learning Diagnosis Diagnostic systems Diameters Disease Health services Image enhancement Image segmentation Medical imaging Neural networks Noise Noise reduction Older people Optimization Tomography Ultrasonic imaging |
| Title | Abdominal Enhanced Computed Tomography Image by Artificial Intelligence Algorithm in the Diagnosis of Abdominal Aortic Aneurysm |
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