SynthesisNeuroradiology2025
Artificial intelligence and machine learning driven segmentation and quantification models for brain arteriovenous malformations: A systematic review.
Synthesis in Neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
Funding
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Abstract
purposeOur 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).
methodsA 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).
resultsThere 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.
conclusionAI 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.
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Registered trials
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