Utility of artificial intelligence in radiosurgery for pituitary adenoma: a deep learning–based automated segmentation model and evaluation of its clinical applicability
The results were applicable for treatment planning with either no or minor manual modifications, demonstrating a significant increase in the efficiency of the planning process. The predicted segmentations can be loaded into the planning software used in clinical practice for treatment planning. The authors discuss some considerations of the clinical utility of the automated segmentation models, as well as their integration within established clinical workflows, and outline directions for future research.
Authors
- Lucie Hamáčková
- Roman Liščák (ORCID: https://orcid.org/0000-0002-0929-8320)
- N Balasubramaniam
- David Netuka (ORCID: https://orcid.org/0000-0001-8609-4789)
- Jan Kybic (ORCID: https://orcid.org/0000-0002-9363-4947)
- Dalibor Síla (ORCID: https://orcid.org/0000-0001-7076-0417)
- Martin Májovský (ORCID: https://orcid.org/0000-0001-7725-5181)
- Josef Novotný (ORCID: https://orcid.org/0000-0001-9556-7162)
- Hana Hallak (ORCID: https://orcid.org/0000-0002-8191-4175)
- Martin Černý (ORCID: https://orcid.org/0000-0002-8601-0554)
- Jaromír May (ORCID: https://orcid.org/0000-0001-9925-338X)
- Denis Baručić (ORCID: https://orcid.org/0000-0003-0428-3354)
- Michaela May (ORCID: https://orcid.org/0000-0002-2709-6159)
- Michael J. Link (ORCID: https://orcid.org/0000-0002-2102-6897)
- Miriam Babničová
Institutions
- Mayo Clinic (US)
- Charles University (CZ)
- King Faisal Specialist Hospital & Research Centre (SA)
- McGill University Health Centre (CA)
- Na Homolce Hospital (CZ)
- Military University Hospital Prague (CZ)
- Neurological Surgery (US)
- Czech Technical University in Prague (CZ)
Publication Details
- Journal
- Journal of neurosurgery
- Published
- 2025-04-18
- DOI
- https://doi.org/10.3171/2024.12.jns242167
- Citations
- 1
- Primary Topic
- Pituitary Gland Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.29
Funders
- Univerzita Karlova v Praze
- Ministerstvo Zdravotnictví Ceské Republiky
- Ministry of Defense