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Artificial Neural Networks: Useful Aid in Diagnosing Acute Appendicitis

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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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Authors and Affiliations

  1. 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. G. Prabhudesai, S. Gould, S. Rekhraj, P. P. Tekkis & P. Ziprin

  2. General Surgical Unit, St. Mary’s Hospital, Praed Street, London, W2 1NY, UK

    G. Glazer

Authors
  1. S. G. Prabhudesai
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  2. S. Gould
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  3. S. Rekhraj
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  4. P. P. Tekkis
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  5. G. Glazer
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  6. P. Ziprin
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Corresponding author

Correspondence to S. G. Prabhudesai.

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Prabhudesai, S.G., Gould, S., Rekhraj, S. et al. Artificial Neural Networks: Useful Aid in Diagnosing Acute Appendicitis. World J Surg 32, 305–309 (2008). https://doi.org/10.1007/s00268-007-9298-6

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  • DOI: https://doi.org/10.1007/s00268-007-9298-6

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