ArticleCNS neuroscience & therapeutics2026
Multimodal Deep Learning and Knowledge-Enhanced Intelligent Decision Support System for Pipeline Embolization Device Size Selection in Intracranial Aneurysm Treatment.
Article in CNS neuroscience & therapeutics, 2026. 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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12 authors.
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Abstract
objectiveThis study aimed to develop an end-to-end intelligent decision support system, NeurAneuNet, to automate the selection of Pipeline Embolization Device (PED) size and landing zones for intracranial aneurysm treatment, thereby reducing reliance on operator experience and improving planning consistency.
methodsNeurAneuNet integrates multimodal deep learning with knowledge enhancement. The system processes three-dimensional rotational angiography (3DRA) images using a dual-path attention U-Net++ for aneurysm segmentation, extracts knowledge-augmented geometric and clinical features, fuses five feature modalities via tensor decomposition, and predicts optimal PED sizing and landing zones with a high-order Kolmogorov-Arnold Network (KAN). A dataset of 600 aneurysms (including 210 PED-treated cases) was used for model development and validation, with an independent clinical cohort of 21 cases employed to assess real-world utility.
resultsNeurAneuNet achieved a Dice coefficient of 0.874 ± 0.03 for aneurysm segmentation, a PED size classification accuracy of 91.8%, and a diameter prediction error of 0.24 ± 0.10 mm. In the independent test, its primary recommendation agreed with the expert consensus in 95.2% of cases. When assisting clinicians, the system reduced planning time by 44.8% and significantly lowered subjective cognitive workload, as assessed by the National Aeronautics and Space Administration Task Load Index (NASA-TLX) score (from 33 ± 8 to 21 ± 5).
conclusionNeurAneuNet demonstrates clinically relevant accuracy and efficiency in automating PED treatment planning, providing robust, intelligent support for intracranial aneurysm interventions. The system provides a promising foundation for developing a generalizable AI-driven decision support framework in complex medical scenarios.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.