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

INTRODUCTION

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.