Grigsby J, Kramer R E, Schneiders J L, Gates J R, Brewster Smith W
University of Colorado Health Sciences Center, Denver 80222, USA.
Epilepsia. 1998 Jan;39(1):61-6. doi: 10.1111/j.1528-1157.1998.tb01275.x.
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).
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.
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%.
Simulated neural networks show promise as adjuncts to decision-making in the selection of epilepsy surgery patients.
颞叶前部切除术（ATL）是治疗药物难治性癫痫的重要选择。患者的选择并非总是明确的，并且与侵入性和昂贵的手术方案相关的固有发病率和死亡率。为了确定人工智能的应用是否有助于患者的选择，我们开发了一种使用模拟神经网络（SNN）预测ATL后癫痫发作结果的模型。
将该模型的预测结果与传统判别函数分析得出的预测结果进行比较。利用来自三个手术中心的87名患者的数据，设计了神经网络和判别函数，以预测1类结果（完全无癫痫发作）以及1类或2类结果（几乎或完全无癫痫发作）的发生情况。使用从65名患者的随机选择子样本中获得的数据开发SNN和判别函数，并使用其余22名患者对两个模型进行交叉验证。
判别函数的总体预测准确率分别为78.5%和72.7%，而神经网络的总体准确率分别为81.8%和95.4%。
模拟神经网络有望作为癫痫手术患者选择决策的辅助手段。
