Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia

The neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the effect of the deep-learning-based color Doppler ul...

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Main Authors: Peng Bian, Xiyu Zhang, Ruihong Liu, Huijie Li, Qingqing Zhang, Baoling Dai
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Journal of Healthcare Engineering
Online Access:http://dx.doi.org/10.1155/2021/2603842
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spelling doaj-61e38b5de65049549d2c6e3f6fffba912021-08-09T00:01:41ZengHindawi LimitedJournal of Healthcare Engineering2040-23092021-01-01202110.1155/2021/2603842Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with SarcopeniaPeng Bian0Xiyu Zhang1Ruihong Liu2Huijie Li3Qingqing Zhang4Baoling Dai5Department of Statistics and Medical Record ManagementDepartment of Statistics and Medical Record ManagementDepartment of Statistics and Medical Record ManagementDepartment of Statistics and Medical Record ManagementDepartment of Statistics and Medical Record ManagementMedical OfficeThe neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the effect of the deep-learning-based color Doppler ultrasound image on the diagnosis of CHF. 259 patients were selected randomly in this study, who were admitted to hospital from October 2017 to March 2020 and were diagnosed with sarcopenia. Then, all of them underwent cardiac ultrasound examination and were divided into two groups according to whether deep learning technology was used for image processing or not. A group of routine unprocessed images was set as the control group, and the images processed by deep learning were set as the experimental group. The results of color Doppler images before and after processing were analyzed and compared; that is, the processed images of the experimental group were clearer and had higher resolution than the unprocessed images of the control group, with the peak signal-to-noise ratio (PSNR) = 20 and structural similarity index measure (SSIM) = 0.09; the similarity between the final diagnosis results and the examination results of the experimental group (93.5%) was higher than that of the control group (87.0%), and the comparison was statistically significant (P<0.05); among all the patients diagnosed with sarcopenia, 88.9% were also eventually diagnosed with CHF and only a small part of them were diagnosed with other diseases, with statistical significance (P<0.05). In conclusion, deep learning technology had certain application value in processing color Doppler ultrasound images. Although there was no obvious difference between the color Doppler ultrasound images before and after processing, they could all make a better diagnosis. Moreover, the research results showed the correlation between CHF and sarcopenia.http://dx.doi.org/10.1155/2021/2603842
collection DOAJ
language English
format Article
sources DOAJ
author Peng Bian
Xiyu Zhang
Ruihong Liu
Huijie Li
Qingqing Zhang
Baoling Dai
spellingShingle Peng Bian
Xiyu Zhang
Ruihong Liu
Huijie Li
Qingqing Zhang
Baoling Dai
Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia
Journal of Healthcare Engineering
author_facet Peng Bian
Xiyu Zhang
Ruihong Liu
Huijie Li
Qingqing Zhang
Baoling Dai
author_sort Peng Bian
title Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia
title_short Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia
title_full Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia
title_fullStr Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia
title_full_unstemmed Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia
title_sort deep-learning-based color doppler ultrasound image feature in the diagnosis of elderly patients with chronic heart failure complicated with sarcopenia
publisher Hindawi Limited
series Journal of Healthcare Engineering
issn 2040-2309
publishDate 2021-01-01
description The neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the effect of the deep-learning-based color Doppler ultrasound image on the diagnosis of CHF. 259 patients were selected randomly in this study, who were admitted to hospital from October 2017 to March 2020 and were diagnosed with sarcopenia. Then, all of them underwent cardiac ultrasound examination and were divided into two groups according to whether deep learning technology was used for image processing or not. A group of routine unprocessed images was set as the control group, and the images processed by deep learning were set as the experimental group. The results of color Doppler images before and after processing were analyzed and compared; that is, the processed images of the experimental group were clearer and had higher resolution than the unprocessed images of the control group, with the peak signal-to-noise ratio (PSNR) = 20 and structural similarity index measure (SSIM) = 0.09; the similarity between the final diagnosis results and the examination results of the experimental group (93.5%) was higher than that of the control group (87.0%), and the comparison was statistically significant (P<0.05); among all the patients diagnosed with sarcopenia, 88.9% were also eventually diagnosed with CHF and only a small part of them were diagnosed with other diseases, with statistical significance (P<0.05). In conclusion, deep learning technology had certain application value in processing color Doppler ultrasound images. Although there was no obvious difference between the color Doppler ultrasound images before and after processing, they could all make a better diagnosis. Moreover, the research results showed the correlation between CHF and sarcopenia.
url http://dx.doi.org/10.1155/2021/2603842
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