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World Journal of Surgery

Published in:

01-02-2008

Artificial Neural Networks: Useful Aid in Diagnosing Acute Appendicitis

Authors: S. G. Prabhudesai, S. Gould, S. Rekhraj, P. P. Tekkis, G. Glazer, P. Ziprin

Published in: World Journal of Surgery | Issue 2/2008

Abstract

Backround

The purpose of the study was to assess the role of artificial neural networks (ANNs) in the diagnosis of appendicitis in patients presenting with acute right iliac fossa (RIF) pain and comparing its performance with the assessment made by experienced clinicians and the Alvarado score.

Methods

After training and testing an ANN, data from 60 patients presenting with suspected appendicitis over a 6-month period to a teaching hospital was collected prospectively. Accuracy of diagnosing appendicitis by the clinician, the Alvarado score, and the ANN was compared.

Results

The sensitivity, specificity, and positive and negative predictive values of the ANN were 100%, 97.2%, 96.0%, and 100% respectively. The ability of the ANN to exclude accurately the diagnosis of appendicitis in patients without true appendicitis was statistically significant compared to the clinical performance (p = 0.031) and Alvarado score of ≥6 (p = 0.004) and nearly significant compared to the Alvarado score of ≥7 (p = 0.063).

Conclusions

ANNs can be an effective tool for accurately diagnosing appendicitis and may reduce unnecessary appendectomies.

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Title: Artificial Neural Networks: Useful Aid in Diagnosing Acute Appendicitis
Authors: S. G. Prabhudesai
S. Gould
S. Rekhraj
P. P. Tekkis
G. Glazer
P. Ziprin
Publication date: 01-02-2008
Publisher: Springer-Verlag
Published in: World Journal of Surgery / Issue 2/2008
Print ISSN: 0364-2313
Electronic ISSN: 1432-2323
DOI: https://doi.org/10.1007/s00268-007-9298-6

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Artificial Neural Networks: Useful Aid in Diagnosing Acute Appendicitis

Abstract

Backround

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Backround

Methods

Results

Conclusions

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