Machine learning for functional outcome prediction after vestibular schwannoma surgery: a systematic review and diagnostic test accuracy meta-analysis
Abstract
Purpose: Machine learning (ML) models have been increasingly applied to predict postoperative facial nerve dysfunction and hearing preservation after vestibular schwannoma (VS) surgery. However, reported performance varies substantially, and the overall diagnostic accuracy and clinical reliability of these models remain uncertain. We conducted a systematic review and diagnostic test accuracy meta-analysis to characterise the current state and methodological readiness of ML-based prediction of these outcomes.
Methods: PubMed, Embase, and CENTRAL were searched from inception to February 2026. Studies evaluating ML-based prediction of facial nerve function or hearing preservation following VS surgery were included. Diagnostic performance metrics were pooled using random-effects generalised linear mixed models. Sensitivity, specificity, diagnostic odds ratio, and AUC were synthesised, and SROC curves were constructed. The prespecified primary synthesis pooled the single best model per study; small-study effects were assessed with Deeks' test. Risk of bias (PROBAST) and certainty of evidence (GRADE) were assessed.
Results: Ten retrospective cohort studies encompassing 1270 patients and 56 ML models met inclusion criteria. In the prespecified primary analysis pooling the single best model per study, the summary AUC was 0.91 for facial nerve dysfunction (sensitivity 0.89, specificity 0.86) and 0.92 for hearing preservation (sensitivity 0.88, specificity 0.96). Pooling all models on…
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health. Reported size: 1,270 participants or records.
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