Quantifying post-treatment vascular remodeling in brain aneurysms using WEKA-based machine learning: a pilot study
The WEKA pipeline enabled quantification of vascular remodeling but remained limited by manual preprocessing and lack of external validation. Machine learning-guided segmentation of angiographic images can detect treatment-induced vascular changes, particularly following endovascular therapy. This method demonstrates promise for future development of automated imaging biomarkers to support outcome monitoring and clinical decision-making in neurovascular care.
Authors
- Ante Rotim
- Silva Butković Soldo (ORCID: https://orcid.org/0000-0002-1671-2956)
- Petar Marčinković
- Marina Raguž (ORCID: https://orcid.org/0000-0001-9203-7254)
- Vladimir Kalousek (ORCID: https://orcid.org/0000-0002-1439-0930)
- Krešimir Rotim
- Bruno Splavski (ORCID: https://orcid.org/0000-0002-4483-9364)
- Darko Orešković
- Tomislav Sajko
- Nikica Fulir
Institutions
- University Hospital Centre Zagreb (HR)
- University of Dubrovnik (HR)
- University Hospital Dubrava (HR)
- University of Applied Health Sciences (HR)
- Catholic University of Croatia (HR)
- University of Osijek (HR)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-10-09
- DOI
- https://doi.org/10.3389/fneur.2025.1650932
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 0.00