✦ Clinical Takeaway ✦
No actionable change — this is an erratum to a published systematic review and meta-analysis; clinicians should consult the corrected version for accurate data on deep learning segmentation performance.
✦ Why It Matters ✦
Accurate AI-based segmentation of vestibular schwannomas is increasingly relevant for treatment planning and monitoring, and corrections to systematic reviews ensure the evidence base remains reliable.
✦ Key Points ✦
- 01Published correction (erratum) to a systematic review and meta-analysis in Neuroradiology (2026).
- 02Original study assessed deep learning model accuracy for vestibular schwannoma segmentation on imaging.
- 03No new data are introduced; errors in the prior publication are being rectified.
- 04Corrected figures or data points should be used in any citations of the original work.
- 05DOI: 10.1007/s00234-026-04056-y.
✦ Research metadata ✦
- PMID
- 42234018
- DOI
- 10.1007/s00234-026-04056-y.
- Journal
- Neuroradiology
- Publication type
- meta_analysis
- Evidence level
- 1a
- Population
- Published studies reporting deep learning segmentation of vestibular schwannoma on MRI
- Intervention
- Deep learning models for automated vestibular schwannoma segmentation
- Comparator
- Manual/expert segmentation (assumed from systematic review context)
Primary outcomes
Segmentation accuracy of deep learning models; Comparison of model performance across studies