Geetinder Kaur

Neural network analysis of anal sphincter repair.

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.