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Updated: Jan 11, 2026
Comprehensive Endovascular and Open Surgical Management of Cerebral Arteriovenous Malformations
Published on: October 20, 2017
Artificial intelligence and machine learning driven segmentation and quantification models for brain arteriovenous
Mehmet Denizhan Yurtluk1, Kishore Balasubramanian2, Matthew P Blackwell3
1Department of Neurological Surgery, Center for Image-Guided Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, United States.
Purpose:
Our systematic review aims to evaluate the application of artificial intelligence (AI) and machine learning (ML) techniques for the automatic segmentation, quantification, and treatment planning of brain arteriovenous malformations (AVMs).
Methods:
A Preferred Reporting Items for systematic reviews and Meta-Analysis (PRISMA) guided systematic review was conducted using specific keywords and Boolean operators across PubMed, ScienceDirect, Scopus, and Web of Science. Studies were included based on the use of AI or machine learning (ML) models for imaging-based analysis of arteriovenous malformations (AVMs).
Results:
There were thirteen studies with 3,010 individuals. The most popular modalities were TOF-MRA and MRI. U-Net, Dense U-Net, YOLO, SVM, and fuzzy c-means clustering were among the models. Across all experiments, the average Dice similarity score was 0.758. The models showed usefulness in bleeding risk assessment, corticospinal tract involvement, AVM diffuseness prediction, nidus segmentation, and stereotactic radiosurgery (SRS) planning. In tasks involving radiation planning and hemorrhagic risk, a number of models provided better or comparable predicted accuracy and showed good agreement with manual segmentations.
Conclusion:
AI and ML show potential for AVM evaluation, with early studies suggesting they may support efficiency and standardization in diagnosis and treatment planning. Despite encouraging findings, model generalizability and clinical implementation remain limited. Future studies should focus on prospective validation, integration of multimodal imaging, and post-treatment segmentation to enhance clinical translation.
