Abstract
BackgroundGenome wide gene expression analysis has revealed hints for independent immunological pathways underlying the pathophysiologies of phlegmonous (PA) and gangrenous appendicitis (GA). Methods of artificial intelligence (AI) have successfully been applied to routine laboratory and sonographic parameters for differentiation of the inflammatory manifestations. In this study we aimed to apply AI methods to gene expression data to provide evidence for feasibility.MethodsModern algorithms from AI were applied to 56.666 gene expression data sets from 13 patients with PA and 16 with GA aged 7–17 years by using resampling methods (bootstrap). Performance with respect to sensitivities and specificities where investigated with receiver operating characteristic (ROC) analysis.ResultsWithin the experimental setting a best performing discriminatory biomarker signature consisting of a set of 4 genes could be defined: ERGIC and golgi 3, regulator of G-protein signaling 2, Rho GTPase activating protein 33, and Golgi Reassembly Stacking Protein 2. ROC analysis showed a mean area under the curve of 84%.ConclusionsGene expression based application of AI methods is feasible and represents a promising approach for future discriminatory diagnostics in children with acute appendicitis.
Highlights
Genome wide gene expression analysis has revealed hints for independent immunological pathways underlying the pathophysiologies of phlegmonous (PA) and gangrenous appendicitis (GA)
New evidence on the pathophysiology of acute appendicitis has led to the concept of risk being given more weight without complications having occurred
The aim of the current study was to investigate the applicability of algorithms from machine learning and artificial intelligence to the extended pool of data from whole genome gene expression analysis for pretherapeutic differentiation of phlegmonous and gangrenous appendicitis in children and adolescents. This prospective study included children aged 7–17 years, which presented with signs of acute appendicitis at the Department of Pediatric Surgery of Charité – Universitätsmedizin Berlin, Germany, between April 2019 and August 2019
Summary
Genome wide gene expression analysis has revealed hints for independent immunological pathways underlying the pathophysiologies of phlegmonous (PA) and gangrenous appendicitis (GA). In this study we aimed to apply AI methods to gene expression data to provide evidence for feasibility. Successful and safe application of conservative antibiotic treatment for clinically uncomplicated appendicitis has been demonstrated [1]. New evidence on the pathophysiology of acute appendicitis has led to the concept of risk being given more weight without complications having (already) occurred. This attitude is especially due to evidence on substantial epidemiological and immunological differences between histopathological phlegmonous and gangrenous
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