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

Peng Bian,Qingqing Zhang,Ruihong Liu,Baoling Dai,Xiyu Zhang,Huijie Li,Enas Abdulhay

doi:10.1155/2021/2603842

Abstract

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.

Highlights

Cardiovascular and cerebrovascular diseases are highmorbidity diseases in the elderly population
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. is study was approved by the Medical Ethics Committee, and all the patients participating in the study signed the informed consent forms
Among all the patients diagnosed with sarcopenia in this study, 88.9% were diagnosed with Chronic heart failure (CHF), and only a small part suffered from other diseases, CNN optimization

Summary

Introduction

Cardiovascular and cerebrovascular diseases are highmorbidity diseases in the elderly population. With the aging of the population in China, the occurrence and mortality of cardiovascular and cerebrovascular diseases are gradually increasing, especially in the aspect of heart. Chronic heart failure (CHF) will appear in the late stage of heart disease development [1]. Sarcopenia, known as age-related sarcopenia, is a complication of developing CHF, which mainly refers to an age-related degenerative syndrome in which the content of human skeletal muscle gradually decreases with the increase of age, as well as the strength and function of muscle gradually deteriorates [3]. Studies have shown that grip strength, muscle strength of quadriceps femoris, 6-minute walking distance, reduced left ventricular ejection fraction, and high oxygen consumption will appear due to low exercise peak when CHF patients are complicated

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Discussion

Conclusion