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 ± 1.27 vs. 3.42 ± 1.04 ), and the perfusion index (PI) was significantly lower than that in the control group ( 134.46 ± 17.29 vs. 168.37 ± 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.

Ultrasonographic Applications of Novel Technologies and Artificial Intelligence in Critically Ill Patients

“…Point-of-care ultrasound (POCUS) has a wide diagnostic application in intensive care, making it easier to use and interpret such imaging modalities as transthoracic and transesophageal echocardiography, lung ultrasound, ultrasound of a patient in shock, and patients with other life-threatening conditions. Due to the complexity of ultrasound assessment in intensive care, the physician’s purpose of using AI in this setting is to help assess the patient’s condition based on US imaging of various systems and organs and assess the patient’s hemodynamic profile [ 4 , 5 , 6 ]. The literature has shown that AI-guided POCUS will most likely increase the efficiency and effectiveness of ultrasound imaging [ 4 , 7 ].…”

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

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”