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Updated: Jul 11, 2025
Facial Nerve Surgery in the Rat Model to Study Axonal Inhibition and Regeneration
Published on: May 5, 2020
An Artificial Neural Network Model for Predicting Postoperative Facial Nerve Outcomes After Vestibular Schwannoma
Youssef M Zohdy1, Ali M Alawieh1, David Bray1
1Department of Neurosurgery, Emory University, Atlanta , Georgia , USA.
Background And Objectives:
The emergence of machine learning models has significantly improved the accuracy of surgical outcome predictions. This study aims to develop and validate an artificial neural network (ANN) model for predicting facial nerve (FN) outcomes after vestibular schwannoma (VS) surgery using the proximal-to-distal amplitude ratio (P/D) along with clinical variables.
Methods:
This retrospective study included 71 patients who underwent VS resection between 2018 and 2022. At the end of surgery, the FN was stimulated at the brainstem (proximal) and internal acoustic meatus (distal) and the P/D was calculated. Postoperative FN function was assessed using the House-Brackmann grading system at discharge (short-term) and after 9-12 months (long-term). House-Brackmann grades I-II were considered good outcome, whereas grades III-VI were considered fair/poor. An ANN model was constructed, and the performance of the model was evaluated using the area under the ROC curve for internal validation and accuracy, sensitivity, specificity, and positive and negative predictive values for external validation.
Results:
The short-term FN outcome was grades I-II in 57.7% and grades III-VI in 42.3% of patients. Initially, a model using P/D had an area under the curve of 0.906 (internal validation) and an accuracy of 89.1% (95% CI: 68.3%-98.8%) (external validation) for predicting good vs fair/poor short-term FN outcomes. The model was then refined to include only muscles with a P/D with a proximal latency between 6 and 8 ms. This improved the accuracy to 100% (95% CI: 79%-100%). Integrating clinical variables (patient's age, tumor size, and preoperative HB grade) in addition to P/D into the model did not significantly improve the predative value. A model was then created to predict the long-term FN outcome using P/D with latencies between 6 and 8 ms and had an accuracy of 90.9% (95% CI: 58.7%-99.8%).
Conclusion:
ANN models incorporating P/D can be a valuable tool for predicting FN outcomes after VS surgery. Refining the model to include P/D with latencies between 6 and 8 ms further improves the model's prediction. A user-friendly interface is provided to facilitate the implementation of this model.
