To explore the value of artificial neural network (ANN) model in predicting the long-term clinical efficacy of microvascular decompression (MVD) in patients with trigeminal neuralgia (TN).

The perioperative data of 1041 TN patients who underwent MVD surgery in Neurosurgery Department of Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine from March 2013 to May 2018 were collected to construct the ANN prediction model. The prediction results are compared with the actual follow-up results to evaluate the performance of the model, then changing the variables of the input layer to find the factors which have the greatest impact on the prediction accuracy of the model.

The ANN model can accurately predict the long-term efficacy of TN patients after MVD with an accuracy rate of 94.2%. These four factors (whether the anatomical location of offending vessels on the trigeminal nerve and the location of facial pain meet, the pain relief in the short term after MVD, the degree of the offending vessels compression, and the type of offending vessels) contribute the most to the prediction performance of ANN. After deleting them in turn, the prediction accuracy of the model dropped to 75.3%, 79.8%, 86.6%, 89.2%.

The ANN model can objectively and accurately predict the long-term clinical efficacy of TN patients after MVD, and the model can evaluate the importance of each factor in the prediction of clinical efficacy.