PURPOSE: Anterior temporal lobectomy (ATL) is an important option for treatment of medically refractory seizures. Patient selection is not always clear-cut, and there is inherent morbidity and mortality associated with the invasive and expensive surgical protocols. To determine whether patient selection might be facilitated by application of artificial intelligence, we developed a model that predicted seizure outcome after ATL, using a simulated neural network (SNN). METHODS: Predictions of the model were compared with predictions derived from conventional discriminant function analysis. Neural networks and discriminant functions were devised that would predict the occurrence of both Class 1 outcomes (totally seizure-free), and Class 1 or Class 2 outcomes (nearly or totally seizure-free), using data from 87 patients from three surgical centers. The SNNs and discriminant functions were developed using data from a randomly selected subsample of 65 patients, and both models were cross-validated, using the remaining 22 patients. RESULTS: The discriminant functions showed overall predictive accuracy of 78.5% and 72.7%, while the neural networks demonstrated overall accuracy of 81.8% and 95.4%. CONCLUSIONS: Simulated neural networks show promise as adjuncts to decision-making in the selection of epilepsy surgery patients.
PURPOSE: Anterior temporal lobectomy (ATL) is an important option for treatment of medically refractory seizures. Patient selection is not always clear-cut, and there is inherent morbidity and mortality associated with the invasive and expensive surgical protocols. To determine whether patient selection might be facilitated by application of artificial intelligence, we developed a model that predicted seizure outcome after ATL, using a simulated neural network (SNN). METHODS: Predictions of the model were compared with predictions derived from conventional discriminant function analysis. Neural networks and discriminant functions were devised that would predict the occurrence of both Class 1 outcomes (totally seizure-free), and Class 1 or Class 2 outcomes (nearly or totally seizure-free), using data from 87 patients from three surgical centers. The SNNs and discriminant functions were developed using data from a randomly selected subsample of 65 patients, and both models were cross-validated, using the remaining 22 patients. RESULTS: The discriminant functions showed overall predictive accuracy of 78.5% and 72.7%, while the neural networks demonstrated overall accuracy of 81.8% and 95.4%. CONCLUSIONS: Simulated neural networks show promise as adjuncts to decision-making in the selection of epilepsy surgery patients.
Authors: Rubén Armañanzas; Lidia Alonso-Nanclares; Jesús Defelipe-Oroquieta; Asta Kastanauskaite; Rafael G de Sola; Javier Defelipe; Concha Bielza; Pedro Larrañaga Journal: PLoS One Date: 2013-04-30 Impact factor: 3.240