Machine learning model for predicting hearing preservation after vestibular schwannoma surgery: A meta-analysis
ML models achieve high accuracy in predicting hearing preservation in VS patients undergoing surgery. However, significant heterogeneity exists across studies, indicating the need for further research to optimize model performance and enhance their generalizability across diverse patient populations. ML has the potential to assist clinicians in providing personalized treatment strategies and improving patient outcomes in the management of vestibular schwannomas.
Authors
- Enzo von Quednow (ORCID: https://orcid.org/0000-0001-6512-1979)
- Walter Fagundes (ORCID: https://orcid.org/0000-0002-1861-3564)
- Fabio Victor Vieira Rocha (ORCID: https://orcid.org/0009-0004-5385-7005)
- Paweł Łajczak (ORCID: https://orcid.org/0000-0003-0281-5417)
- Oguz Kagan Sahin (ORCID: https://orcid.org/0009-0006-0001-0390)
- Emma Ann Finnegan (ORCID: https://orcid.org/0009-0009-3634-5119)
- Yasmin Picanço Silva (ORCID: https://orcid.org/0009-0003-5728-4964)
Institutions
- Medical University of Silesia (PL)
- Trinity College Dublin (IE)
- State Hospital (GB)
- Hospital Universitari Sant Joan de Reus (ES)
- Universidade Federal do Espírito Santo (BR)
Publication Details
- Journal
- American Journal of Otolaryngology
- Published
- 2025-10-03
- DOI
- https://doi.org/10.1016/j.amjoto.2025.104727
- Primary Topic
- Meningioma and schwannoma management
- Type
- article
- Field-Weighted Citation Impact
- 0.00