Appendicitis Research Today is a free monthly online journal that collates and summarizes the latest research about Appendicitis, including details on symptoms, surgery, causes, treatment.

Artificial Neural Networks: Useful Aid in Diagnosing Acute Appendicitis.

Prabhudesai SG, Gould S, Rekhraj S, Tekkis PP, Glazer G, Ziprin P

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.

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.

Published 21 January 2008 in World J Surg, 32(2): 305-309.
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