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Parisa Azimi and Hasan Reza Mohammadi

Object

Artificial neural networks (ANNs) can be used as a measure for the clinical decision-making process. The aim of this study was to develop an ANN model to predict endoscopic third ventriculostomy (ETV) success at 6 months and to compare the findings with those obtained using traditional predictive measures in childhood hydrocephalus.

Methods

The ANN, ETV Success Score (ETVSS), CURE Children's Hospital of Uganda (CCHU) ETV (CCHU ETV) Success Score, and logistic regression models were applied to predict outcomes. The cause of hydrocephalus, patient age, whether choroid plexus cauterization (CPC) was performed, previous shunt surgery, sex, type of hydrocephalus, and body weight were considered as input variables for an established ANN model. Data from hydrocephalic children who underwent ETV were applied, and the computer program that analyzes the data was trained to predict successful ETV by using several input variables. Successful ETV outcome was defined as the absence of ETV failure within 6 months of follow-up. Then, sensitivity analysis was performed for the established ANN model to identify the most important variables that predict outcome. The area under a receiver operating characteristic curve, accuracy rate of the prediction, and Hosmer-Lemeshow statistics were measured to test different prediction models.

Results

Data for 168 patients (80 males and 88 females; mean age 1.4 ± 2.6 years) were analyzed. Data from patients were divided into 3 groups: a training group (n = 84), a testing group (n = 42), and a validation group (n = 42). The successful ETV outcome rate, defined as the absence of ETV failure within 6 months of follow-up, was 47%. Etiology, age, CPC status, type of hydrocephalus, and previous shunt placement were the most important variables that were indicated by the ANN analysis. Compared with the ETVSS, CCHU ETV Success Score, and the logistic regression models, the ANN model showed better results, with an accuracy rate of 95.1%, a Hosmer-Lemeshow statistic of 41.2, and an area under the curve of 0.87.

Conclusions

The findings show that ANNs can predict ETV success at 6 months with a high level of accuracy in childhood hydrocephalus. The authors' results will need to be confirmed with further prospective studies.

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Parisa Azimi, Edward C. Benzel, Sohrab Shahzadi, Shirzad Azhari and Hasan Reza Mohammadi

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.