Object The purpose of this study was to develop an artificial neural network (ANN) model for predicting 2-year surgical satisfaction, and to compare the new model with traditional predictive tools in patients with lumbar spinal canal stenosis. Methods The 2 prediction models included an ANN and a logistic regression (LR) model. The patient age, sex, duration of symptoms, walking distance, visual analog scale scores of leg pain or numbness, the Japanese Orthopaedic Association score, the Neurogenic Claudication Outcome Score, and the stenosis ratio values were determined as the input variables for the ANN and LR models that were developed. Patient surgical satisfaction was recorded using a standardized measure. The ANNs were fed patient data to predict 2-year surgical satisfaction based on several input variables. Sensitivity analysis was applied to the ANN model to identify the important variables. The receiver operating characteristic–area under curve (ROC-AUC), Hosmer-Lemeshow statistics, and accuracy rate were calculated for evaluating the 2 models. Results A total of 168 patients (59 male, 109 female; mean age 59.8 ± 11.6 years) were divided into training (n = 84), testing (n = 42), and validation (n = 42) data sets. Postsurgical satisfaction was 88.7% at 2-year follow-up. The stenosis ratio was the important variable selected by the ANN. The ANN model displayed a better accuracy rate in 96.9% of patients, a better Hosmer-Lemeshow statistic in 42.4% of patients, and a better ROC-AUC in 80% of patients, compared with the LR model. Conclusions The findings show that an ANN can predict 2-year surgical satisfaction for use in clinical application and is more accurate compared with an LR model.

Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literature review of all full publications in English biomedical journals (1993-2013) was undertaken. The strategy included a combination of key words 'artificial neural networks', 'prognostic', 'brain', 'tumor tracking', 'head', 'tumor', 'spine', 'classification' and 'back pain' in the title and abstract of the manuscripts using the PubMed search engine. The major findings are summarized, with a focus on the application of ANNs for diagnostic and prognostic purposes. Finally, the future of ANNs in neurosurgery is explored. A total of 1093 citations were identified and screened. In all, 57 citations were found to be relevant. Of these, 50 articles were eligible for inclusion in this review. The synthesis of the data showed several applications of ANN in neurosurgery, including: (1) diagnosis and assessment of disease progression in low back pain, brain tumours and primary epilepsy; (2) enhancing clinically relevant information extraction from radiographic images, intracranial pressure processing, low back pain and real-time tumour tracking; (3) outcome prediction in epilepsy, brain metastases, lumbar spinal stenosis, lumbar disc herniation, childhood hydrocephalus, trauma mortality, and the occurrence of symptomatic cerebral vasospasm in patients with aneurysmal subarachnoid haemorrhage; (4) the use in the biomechanical assessments of spinal disease. ANNs can be effectively employed for diagnosis, prognosis and outcome prediction in neurosurgery.

Endonasal endoscopic repair of a frontal sinus leak was a successful procedure, with a low failure rate and minimal morbidity.

No abstract