Neural Network Analysis of Anal
Sphincter Repair |
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Authors: | A?Gardiner G?Kaur J?Cundall Email author" target="_blank">G S?DuthieEmail author |
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Institution: | (1) Academic Surgical Unit, Castle Hill Hospital, University of Hull Postgraduate Medical School, Cottingham, United Kingdom |
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Abstract: | PURPOSE: Prediction of success after anterior sphincter repair
for incontinence is difficult. Standard multivariate
analysis techniques have only 75 to 80 percent accuracy.
Artificial intelligence, including artificial neural networks,
has been used in the analysis of complex clinical data and
has proved to be successful in predicting the outcome of
other surgical procedures. Using a neural network algorithm,
we have assessed the probability of success after
anterior sphincter repair. METHODS: Prospective anorectal
physiology data of 72 patients undergoing anterior sphincter
repair was collected between 1995 and 1999. Complete
data sets of 75 percent of the series were used to train an
artificial neural network; the remaining 25 percent were
used for data validation. The output was continence grading,
ranging from 0 to 4 (worse to continent). RESULTS: The
outcome at 3, 6, and 12 months postoperatively was obtained
and assessed. The best correlation between actual
data value and artificial neural network value was found at
12 months (r = 0.931; P = 0.0001). Clear correlations also
were found at three months (r = 0.898; P = 0.0001) and six
months (r = 0.742; P = 0.002). Results of applying a net to
details excluding pudendal nerve latency were poor. CONCLUSIONS:
Artificial neural networks are more accurate (93
percent correlation) than standard statistics (75 percent)
when applied to the prediction of outcome after anterior
sphincter repair. This assessment also confirms the usefulness
of pudendal latency in the prediction of anterior
sphincter repair outcome. The results obtained highlight
the obvious usefulness of artificial neural networks, which
could now be used in a prospective evaluation for application
of the technique. |
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Keywords: | Neural networks Incontinence Anterior anal sphincter repair Preoperative variables Outcome |
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