Artificial intelligence for personalized management of vestibular schwannoma: a multidisciplinary clinical implementation study
This pilot clinical implementation study highlights the potential benefits of integrating automated measurements into clinical decision-making for VS management.
Authors
- Navodini Wijethilake (ORCID: https://orcid.org/0000-0001-9620-8233)
- Anna Oviedova
- K. Chia
- Rebecca Burger
- Omar Al-Salihi
- Christoforos Syrris (ORCID: https://orcid.org/0000-0001-8297-2126)
- Eleni Maratos
- Tom Vercauteren (ORCID: https://orcid.org/0000-0003-1794-0456)
- Theofanis Giannis (ORCID: https://orcid.org/0009-0000-6150-4913)
- Ahmed B. Abougamil (ORCID: https://orcid.org/0000-0002-6274-7519)
- Dan Jiang (ORCID: https://orcid.org/0000-0002-8899-6425)
- Marina Ivory
- Steve Connor (ORCID: https://orcid.org/0000-0001-5502-4972)
- Amanda Hitchings
- Jeromel De Leon De Sagun
- Sinan Barazi
- Rupert Obholzer
- Nick Thomas
- Jonathan Shapey
Institutions
- King's College London (GB)
- Guy's and St Thomas' NHS Foundation Trust (GB)
- King's College Hospital NHS Foundation Trust (GB)
- King's College School (GB)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-11-24
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf163
- Primary Topic
- Meningioma and schwannoma management
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Wellcome Trust
- Royal Academy of Engineering
- King's College London
- Medical Research Council
- Engineering and Physical Sciences Research Council