Artificial neural networks: useful aid in diagnosing acute appendicitis

World J Surg. 2008 Feb;32(2):305-9; discussion 310-1. doi: 10.1007/s00268-007-9298-6.


Background: [corrected] 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 >or=6 (p=0.004) and nearly significant compared to the Alvarado score of >or=7 (p=0.063).

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

Publication types

  • Controlled Clinical Trial

MeSH terms

  • Adult
  • Appendectomy
  • Appendicitis / complications
  • Appendicitis / diagnosis*
  • Appendicitis / surgery
  • Cohort Studies
  • Female
  • Flank Pain / etiology
  • Humans
  • Laparoscopy
  • Male
  • Neural Networks, Computer*
  • Predictive Value of Tests
  • Sex Factors