Ultrasonic Image Features under the Intelligent Algorithm in the Diagnosis of Severe Sepsis Complicated with Renal Injury

Abstract

This research was aimed at analyzing the diagnosis of severe sepsis complicated with acute kidney injury (AKI) by ultrasonic image information based on the artificial intelligence pulse-coupled neural network (PCNN) algorithm and at improving the diagnostic accuracy and efficiency of clinical severe sepsis complicated with AKI. In this research, 50 patients with sepsis complicated with AKI were collected as the observation group and 50 patients with sepsis as the control group. All patients underwent ultrasound examination. The clinical data of the two groups were collected, and the scores of acute physiology and chronic health assessment (APACHE II) and sequential organ failure assessment (SOFA) were compared. The ultrasonic image information enhancement algorithm based on artificial intelligence PCNN is constructed and simulated and is compared with the maximum between-class variance (OSTU) algorithm and the maximum entropy algorithm. The results showed that the PCNN algorithm was superior to the OSTU algorithm and maximum entropy algorithm in the segmentation results of severe sepsis combined with AKI in terms of regional consistency (UM), regional contrast (CM), and shape measure (SM). The acute physiology and chronic health evaluation (APACHE II) and sequential organ failure assessment (SOFA) scores in the observation group were substantially higher than those in the control group ( $P<0.05$ ). The interlobular artery resistance index (RI) in the observation group was substantially higher than that in the control group ( $P<0.05$ ). Moreover, the mean transit time (mTT) in the observation group was significantly higher than that in the control group ( $4.85\pm 1.27$ vs. $3.42\pm 1.04$ ), and the perfusion index (PI) was significantly lower than that in the control group ( $134.46\pm 17.29$ vs. $168.37\pm 19.28$ ), with statistical significance ( $P<0.05$ ). In summary, it can substantially increase ultrasonic image information based on the artificial intelligence PCNN algorithm. The RI, mTT, and PI of the renal interlobular artery level in ultrasound images can be used as indexes for the diagnosis of severe sepsis complicated with AKI.