• S G Prabhudesai, S Gould, S Rekhraj, P P Tekkis, G Glazer, and P Ziprin.
• Department of Biosurgery and Surgical Technology, Faculty of Medicine, Imperial College London, St. Mary's Hospital Campus, Room 1029, 10th floor QEQM Building, Praed Street, London, W2 1NY, UK. s.prabhudesai@imperial.ac.uk
• World J Surg. 2008 Feb 1;32(2):305-9; discussion 310-1.

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.MethodsAfter 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.ResultsThe 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).ConclusionsANNs can be an effective tool for accurately diagnosing appendicitis and may reduce unnecessary appendectomies.

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