Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0–17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today’s therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters.

Objective: This study aims to differentiate acute uncomplicated and complicated appendicitis, by investigating the correlation between sonographic findings and histological results in different types of paediatric appendicitis. Methods: This is a retrospective study of 1017 paediatric patients (age < 18 years) who underwent ultrasound by paediatric radiologists before appendicectomy at our institution between 2006 and 2016. Histologically, uncomplicated appendicitis was primarily associated with transmural infiltration of neutrophil granulocytes, while complicated appendicitis was characterised by transmural myonecrosis. Logistic regression analyses were used to investigate the association between sonographic and histological findings. Results: Out of 566 (56%) male and 451 (44%) female patients with a mean age of 10.7 years, uncomplicated appendicitis was histologically diagnosed in 446 (44%) children and complicated appendicitis was diagnosed in 348 (34%) cases. The following ultrasound findings were significantly associated with complicated appendicitis in multivariate regression: an increased appendiceal diameter (OR = 1.3, p < .001), periappendiceal fat inflammation (OR = 1.5, p = 0.02), the presence of an appendicolith (OR = 1.7, p = 0.01) and a suspected perforation (OR = 6.0, p < .001) by the pediatric radiologist. For complicated appendicitis, an appendiceal diameter of more than 6 mm had the highest sensitivity (98%), while a sonographically suspected perforation showed the highest specificity (94%). Conclusion: Abdominal sonography by paediatric radiologists can differentiate between uncomplicated and complicated appendicitis in paediatric patients by using an increased appendiceal diameter, periappendiceal fat inflammation, the presence of an appendicolith and a suspected perforation as discriminatory markers. Advances in knowledge: This paper demonstrates expanded information on ultrasound, which is not only an essential tool for diagnosing appendicitis, but also a key method for distinguishing between different forms of appendicitis when performed by paediatric radiologists. Compared with previous studies, the crucial distinction features in our analysis are 1) the definition of gangrene and not primarily perforation as an acute complicated appendicitis enabling early decision-making by sonography and 2) a large number of patients in a particularly affected age group.

Background Phlegmonous and gangrenous appendicitis represent independent pathophysiological entities with different clinical courses ranging from spontaneous resolution to septic disease. However, reliable predictive methods for these clinical phenotypes have not yet been established. In an attempt to provide pathophysiological insights into the matter, a genomewide gene expression analysis was undertaken in patients with acute appendicitis. Methods Peripheral blood mononuclear cells were isolated and, after histological confirmation of PA or GA, analysed for genomewide gene expression profiling using RNA microarray technology and subsequent pathway analysis. Results Samples from 29 patients aged 7–17 years were included. Genomewide gene expression analysis was performed on 13 samples of phlegmonous and 16 of gangrenous appendicitis. From a total of 56 666 genes, 3594 were significantly differently expressed. Distinct interaction between T and B cells in the phlegmonous appendicitis group was suggested by overexpression of T cell receptor α and β subunits, CD2, CD3, MHC II, CD40L, and the B cell markers CD72 and CD79, indicating an antiviral mechanism. In the gangrenous appendicitis group, expression of genes delineating antibacterial mechanisms was found. Conclusion These results provide evidence for different and independent gene expression in phlegmonous and gangrenous appendicitis in general, but also suggest distinct immunological patterns for the respective entities. In particular, the findings are compatible with previous evidence of spontaneous resolution in phlegmonous and progressive disease in gangrenous appendicitis.

Background Genome 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. Methods Modern 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. Results Within 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%. Conclusions Gene expression based application of AI methods is feasible and represents a promising approach for future discriminatory diagnostics in children with acute appendicitis.