Artificial neural networks: useful aid in diagnosing acute appendicitis.
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.
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
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Prabhudesai
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Gould
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Rekhraj
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Tekkis
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Glazer
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Ziprin
D000328:Adult; D001062:Appendectomy; D001064:Appendicitis / Q000150:complications / Q000175:diagnosis* / Q000601:surgery; D015331:Cohort Studies; D005260:Female; D021501:Flank Pain / Q000209:etiology; D006801:Humans; D010535:Laparoscopy; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012737:Sex Factors