Quantifying post-treatment vascular remodeling in brain aneurysms using WEKA-based machine learning: a pilot study

To evaluate the feasibility of a WEKA-based machine learning pipeline for detecting post-treatment hemodynamic remodeling by comparing pre- and postoperative cerebral angiographic images in patients with middle cerebral artery aneurysms.

This retrospective, single-center study analyzed 60 patients (51 women, 9 men; mean age, 58.2 ± 10.2 years) with unruptured middle cerebral artery aneurysms treated between January 2019 and June 2024. Thirty patients underwent microsurgical clipping, and 29 underwent endovascular intervention. A WEKA-based Random Forest classifier was trained on 15 manually annotated pre- and postoperative digital subtraction angiography (DSA) image pairs and then applied to the remaining dataset. Custom Python-based post-processing was used to denoise and refine the segmented images. Vascular surface area changes were assessed by comparing pixel counts before and after treatment. Statistical analysis included paired and unpaired t-tests, Mann-Whitney U tests, and effect size estimation.

Among 51 analyzable image pairs, 75% showed increased vascular pixel counts postoperatively, particularly in the endovascular group (segmented pixels: p = 0.034; refined pixels: p = 0.017). No statistically significant differences were observed in the neurosurgical group. Between-group comparisons of postoperative images did not reach significance.

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.