scholarly journals Artificial Intelligence Pulse Coupled Neural Network Algorithm in the Diagnosis and Treatment of Severe Sepsis Complicated with Acute Kidney Injury under Ultrasound Image

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Fu Ying ◽  
Shuhua Chen ◽  
Guojun Pan ◽  
Zemin He
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Acute Kidney Injury ◽  
Severe Sepsis ◽  
Ultrasound Image ◽  
Kidney Injury ◽  
Ultrasound Images ◽  
Image Information ◽  
Ultrasonic Image ◽  
Pulse Coupled Neural Network

The objective of this study was to explore 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. In this study, an algorithm of ultrasonic image information enhancement based on the artificial intelligence PCNN was constructed and compared with the histogram equalization algorithm and linear transformation algorithm. After that, it was applied to the ultrasonic image diagnosis of 20 cases of severe sepsis combined with AKI in hospital. The condition of each patient was diagnosed by ultrasound image performance, change of renal resistance index (RRI), ultrasound score, and receiver operator characteristic curve (ROC) analysis. It was found that the histogram distribution of this algorithm was relatively uniform, and the information of each gray level was obviously retained and enhanced, which had the best effect in this algorithm; there was a marked individual difference in the values of RRI. Overall, the values of RRI showed a slight upward trend after admission to the intensive care unit (ICU). The RRI was taken as the dependent variable, time as the fixed-effect model, and patients as the random effect; the parameter value of time was between 0.012 and 0.015, p = 0.000 < 0.05 . Besides, there was no huge difference in the ultrasonic score among different time measurements (t = 1.348 and p = 0.128 > 0.05 ). The area under the ROC curve of the RRI for the diagnosis of AKI at the 2nd day, 4th day, and 6th day was 0.758, 0.841, and 0.856, respectively, which was all greater than 0.5 ( p < 0.05 ). In conclusion, the proposed algorithm in this study could significantly enhance the amount of information in ultrasound images. In addition, the change of RRI values measured by ultrasound images based on the artificial intelligence PCNN was associated with AKI.

scholarly journals Injúria renal aguda durante sepse grave em ambiente hospitalar / Acute kidney injury during severe sepsis in a hospital setting

2020 ◽  
Vol 3 (5) ◽  
pp. 13557-13564
Author(s):  
Bárbara Caldeira Pires ◽  
Noelly Mayra Silva de Carvalho ◽  
Joice Ribeiro Lopes ◽  
Guilherme Abreu Azevedo ◽  
Kamilla Linhares Silva
Keyword(s):  
Acute Kidney Injury ◽  
Severe Sepsis ◽  
Hospital Setting ◽  
Kidney Injury

Impact of Severe Sepsis on Serum and Urinary Biomarkers of Acute Kidney Injury in Critically Ill Children: An Observational Study

2013 ◽  
Vol 35 (1-3) ◽  
pp. 172-176 ◽  
Author(s):  
Matteo Di Nardo ◽  
Alessio Ficarella ◽  
Zaccaria Ricci ◽  
Rosa Luciano ◽  
Francesca Stoppa ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Severe Sepsis ◽  
Observational Study ◽  
Critically Ill ◽  
Kidney Injury ◽  
Urinary Biomarkers ◽  
Critically Ill Children

Prediction of acute kidney injury using artificial intelligence: are we there yet?

2019 ◽  
Vol 35 (2) ◽  
pp. 204-205 ◽  
Author(s):  
Wim Van Biesen ◽  
Jill Vanmassenhove ◽  
Johan Decruyenaere
Keyword(s):  
Artificial Intelligence ◽  
Acute Kidney Injury ◽  
Kidney Injury

scholarly journals Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Xing Song ◽  
Alan S. L. Yu ◽  
John A. Kellum ◽  
Lemuel R. Waitman ◽  
Michael E. Matheny ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Acute Kidney Injury ◽  
Kidney Injury ◽  
Major Barrier ◽  
Injury Prediction ◽  
The Us ◽  
Performance Deterioration ◽  
Research Gap ◽  
Clinical Adoption ◽  
Cross Site

Abstract Artificial intelligence (AI) has demonstrated promise in predicting acute kidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability. Non-interoperable data across hospitals is a major barrier to model transportability. Here, we leverage the US PCORnet platform to develop an AKI prediction model and assess its transportability across six independent health systems. Our work demonstrates that cross-site performance deterioration is likely and reveals heterogeneity of risk factors across populations to be the cause. Therefore, no matter how accurate an AI model is trained at the source hospital, whether it can be adopted at target hospitals is an unanswered question. To fill the research gap, we derive a method to predict the transportability of AI models which can accelerate the adaptation process of external AI models in hospitals.

scholarly journals Association between recurrence of acute kidney injury and mortality in intensive care unit patients with severe sepsis

2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Emilio Rodrigo ◽  
Borja Suberviola ◽  
Miguel Santibáñez ◽  
Lara Belmar ◽  
Álvaro Castellanos ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Acute Kidney Injury ◽  
Severe Sepsis ◽  
Intensive Care ◽  
Kidney Injury

Positive fluid balance as a prognostic factor for mortality and acute kidney injury in severe sepsis and septic shock

2015 ◽  
Vol 30 (1) ◽  
pp. 97-101 ◽  
Author(s):  
Fernando Saes Vilaça de Oliveira ◽  
Flavio Geraldo Resende Freitas ◽  
Elaine Maria Ferreira ◽  
Isac de Castro ◽  
Antonio Toneti Bafi ◽  
...  
Keyword(s):  
Septic Shock ◽  
Acute Kidney Injury ◽  
Severe Sepsis ◽  
Prognostic Factor ◽  
Fluid Balance ◽  
Kidney Injury ◽  
Positive Fluid Balance ◽  
Sepsis And Septic Shock
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