Account Of Deep Learning-Based Ultrasonic Image Feature in The Diagnosis Of Severe Sepsis Complicated With Acute Kidney Injury

This study was aimed at analyzing the diagnostic value of convolutional neural network models on account of deep learning for severe sepsis complicated with acute kidney injury and providing an effective theoretical reference for the clinical use of ultrasonic image diagnoses. 50 patients with severe sepsis complicated with acute kidney injury and 50 healthy volunteers were selected in this study. They all underwent ultrasound scans. Different deep learning convolutional neural network models dense convolutional network (DenseNet121), Google inception net (GoogLeNet), and Microsoft's residual network (ResNet) were used for training and diagnoses. Then, the diagnostic results were compared with professional image physicians' artificial diagnoses. The results showed that the accuracy and sensitivity of the three deep learning algorithms were significantly higher than professional image physicians' artificial diagnoses. Besides, the error rates of the three algorithm models for severe sepsis complicated with acute kidney injury were significantly lower than professional physicians' artificial diagnoses. The areas under curves (AUCs) of the three algorithms were significantly higher than the AUCs of doctors' diagnosis results. The loss function parameters of DenseNet121 and GoogLeNet were significantly lower than that of ResNet, with a statistically significant difference (P < 0:05). There was no significant difference in training time of ResNet, GoogLeNet, and DenseNet121 algorithms under deep learning, as the convergence was reached after 700 times, 700 times, and 650 times, respectively (P > 0:05). In conclusion, the value of the three algorithms on account of deep learning in the diagnoses of severe sepsis complicated with acute kidney injury was higher than professional physicians' artificial judgments and had great clinical value for the diagnoses and treatments of the disease.

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1. Introduction 

Now, as sepsis is better understood, it is defined by scholars as a clinical syndrome in which an organism's inflammatory response is maladjusted by infection, resulting in severe damage to physiology and organ functions. Sepsis has extremely high morbidity and mortality in intensive care units, and it has become the leading cause of death for critically ill patients [1]. According to some studies, there are more than 30 million new cases of concentrated diseases in the world every year, and more than five million people have been killed by sepsis, which has caused serious pressure and burden on global public health [2–4]. Its pathogeneses and pathological processes are complicated and closely related to inflammation, coagulation dysfunction, and immune disorders [5–7]. Among them, the uncontrolled inflammatory response is considered to be one of the pathogenes of sepsis. Early inflammatory responses released a large number of proinflammatory cytokines and anti-inflammatory cytokines [8]. However, there is often an imbalance between pro-inflammatory factors and anti-inflammatory factors in sepsis patients, and the inflammatory reaction is out of control, which accelerates the development of sepsis [9–11]. In addition, inflammation often interacts with coagulation dysfunction and then influences the development of sepsis. The decreased expression of tissue factors cannot initiate the exogenous coagulation pathway, which results in coagulation disorders and accelerates the formation of vascular damage [12–14].

Acute kidney injury is usually characterized by a rapid decline in renal function, which results in acute kidney failure and other organ failure in severe cases. Acute kidney injury can be caused by a variety of factors, which include drug use, ischemia/reperfusion, and infection [15–17]. In recent years, the incidence and mortality of acute kidney injury have been increasing, and the mortality rate of severe acute kidney injury can reach more than 50%. The pathogenes of acute kidney injury are often related to their pathogenic factors. During organ transplantation, acute blood loss, or toxic shock, ischemia/reperfusion injury has become an important pathogenic mechanism that leads to acute kidney injury [18]. As the most important excretory organ in the human body, drugs are often excreted through the kidney, and their massive use or even abuse is likely to cause drug-induced acute kidney injury [19]. Sepsis complicated with acute kidney injury refers to acute renal parenchymal injury occurring to patients with sepsis, and other factors that may cause kidney damage like renal ischemia or nephrotoxic substances are excluded. Acute kidney injury is quite common in people with sepsis, and the incidence increases with the severity of sepsis. Epidemiological data show that the incidence of acute kidney injury is 19%, 23%, and 51% in patients with moderate sepsis, severe sepsis, and septic shock, respectively. Given the high incidence of sepsis, it can be estimated that the number of acute kidney injury cases induced by sepsis is quite alarming. Compared with other causes, sepsis gives more unstable hemodynamics of acute kidney injury; the proportion of patients who need vasopressors and mechanical ventilation is higher, the disease severity score is higher, and the mortality is also significantly increased ultimately. Delays in early diagnoses and treatments lead to the continuous progression of the disease, and continuous hypoperfusion leads to acute tubular necrosis, which eventually develops into irreversible damage, even in the patient's death [20–22].

Clinically, early diagnoses are often made by creatinine and urine volume detections according to international guidelines, but it is usually unable to make a correct diagnosis in time and completely. With the continuous development of image examination, ultrasound image examination is gradually applied in the clinical diagnoses of sepsis complicated with acute kidney injury. Clinical workers are often unable to sum up quantitative and accurate medical information from ultrasonic images by the naked eye. Medical image analyses and processing technologies solve this dilemma and become important helpers of clinical diagnoses [23–25]. The purpose of further analyses and clarification is to help clinicians diagnose the disease more accurately and quickly and obtain more in-depth information of the disease. A convolutional neural network, a kind of deep neural network, consists of a deeper grid structure that can read image data as visual pathological features and find features that human eyes cannot read. This is very important for ultrasonography in the diagnosis of sepsis complicated with acute kidney injury.

This study was intended to analyze the diagnostic value of severe sepsis complicated with acute kidney injury under a deep learning-based convolutional neural network, to provide a certain reference for the clinical ultrasound image diagnosis.

2. Materials and Methods 

2.1. Study Objects.

In this study, 50 patients with severe sepsis complicated with acute kidney injury admitted to the hospital from January 10, 2020, to May 10, 2021, were selected as the experimental group. According to the age and gender distribution of these patients, 50 healthy volunteers were also selected as the healthy control group. This study had been approved by the ethics committee of the hospital, and patients' families had been informed of this study and signed informed consent.

The inclusion criteria were as follows. First, patients were diagnosed as sepsis complicated with acute kidney injury according to the diagnostic criteria. Second, patients had signed informed consent forms. Third, patients did not suffer from other serious organ diseases or hereditary diseases. Fourth, patients were not examined for contraindications.

The patients who met the exclusion criteria had severe allergies, other serious underlying diseases, and a history of chronic kidney injury. Moreover, the patients took diuretics for a long time.

There were two requirements in the criterion for suspension and elimination. First, patients could not normally complete ultrasound scans. Second, patients who did not comply with the treatments were followed up for index evaluation.

For healthy control group volunteers, the inclusion criterion was the same as those for patients with sepsis complicated with acute kidney injury (2nd-4th). The exclusion criterion was the same as those for patients with sepsis complicated with acute kidney injury (2nd-4th). Patients with acute kidney injury complicated with sepsis were discontinued and excluded.

Figure 5: Comparison of accuracy, specificity, and sensitivity between the three algorithms and professional physicians. Note: ∗ represented significant differences: P < 0:05.

2.2. Ultrasonic Image Examinations.

Ultrasound image examinations were performed in 100 patients at the same time. The patients should be examined with empty stomachs, did not drink a lot of water before the examination, and lay on the examination bed in the supine position and the left lateral position. An ultrasound system and convex array 3.5 MHZ probe were applied for a renal ultrasound examination. The probe was placed in the posterior axillary line, and the position and angle of the probe were adjusted to get the largest coronal image of the kidney. The probe was rotated in 90° at the coronal section and was moved up and down to adjust the angle of the sound beam, then the cross-sectional image of the kidney was obtained. When the patients were in the prone position, the probe was placed under the ribs of the back for longitudinal scanning. With the probe mark facing toward the head, the sagittal plane of the kidney could be observed.

2.3. Convolutional Neural Network Models. 

The Dense-Net121 convolutional neural network structure models completed classification processes through convolution, maximum pooling layers, dense modules, and complete connection layers, respectively. Due to the tight connection between different levels, the DenseNet121 model could absorb and use features of each level and overcome problems of gradient disappearance to a certain extent. The specific grid structure model of DenseNet121 is shown in Figure 1.

In the structure model of the GoogLeNet convolutional neural network, the number of network layers was significantly increased, but there were few parameters. There was an integrated inception module that could combine the pooling layer and convolution layer to achieve fast computing speed and obtain more feature information. Additionally, there were a large number of inception branches. Their structures and characteristics were different, and the final calculation results were more accurate. The inception module of GoogLeNet is shown in Figure 2.

ResNet was an excellent object detection, image classification, and segmentation model that had been widely used in convolutional neural networks. Residual structures appeared in the ResNet model, which made it easier to optimize. In the propagation process of neural networks, the propagation gradient gradually disappeared due to the appearance of backpropagation. Since the existence of residual structures solved this problem, the gradient information was more easily transmitted in the process of reverse transmission of residual structures, and the network with residual modules would get higher identification accuracy. At the same time, the ResNet residual network model adopted a large number of relatively standardized methods of enzyme training. Its specific structural model is shown in Figure 3.

Figure 7: ROC curve results of the three algorithms and diagnoses by professional physicians.

ResNet improved the number of network layers through residual structures and simplified the learning objects to realize the improvement of training speed and the accuracy of parameters. It was suggested to input the initial value xi and set the weight to a. The bias was represented by c, yi was the branch sum, and its calculation functions were shown in the following equations:

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