ArticleJournal of clinical monitoring and computing2023
Neural networks for estimation of facial palsy after vestibular schwannoma surgery.
Article in Journal of clinical monitoring and computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of artificial intelligence in predicting the clinical outcomes associated with different therapeutic approaches for vestibular schwannoma: A systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Automated Neuromuscular Assessment: Machine-Learning-Based Facial Palsy Classification Using Surface Electromyography.Sensors (Basel, Switzerland) · 2025Article
- Review
- Intraoperative transcranial facial motor evoked potential in vestibular schwannoma reflects short-term post operative facial nerve function.Journal of clinical monitoring and computing · 2025Article
- Development and application of explainable artificial intelligence using machine learning classification for long-term facial nerve function after vestibular schwannoma surgery.Journal of neuro-oncology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
purposeFacial nerve damage in vestibular schwannoma surgery is associated with A-train patterns in free-running EMG, correlating with the degree of postoperative facial palsy. However, anatomy, preoperative functional status, tumor size and occurrence of A-trains clusters, i.e., sudden A-trains in most channels may further contribute. In the presented study, we examine neural networks to estimate postoperative facial function based on such features.
methodsData from 200 consecutive patients were used to train neural feed-forward networks (NN). Estimated and clinical postoperative House and Brackmann (HB) grades were compared. Different input sets were evaluated.
resultsNetworks based on traintime, preoperative HB grade and tumor size achieved good estimation of postoperative HB grades (chi
conclusionNN based on preoperative HB, traintime and tumor size provide good estimations of postoperative HB. The method is amenable to real-time implementation and supports integration of information from different sources. NN could enable multimodal facial nerve monitoring and improve postoperative outcomes.
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Registered trials
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