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Develop and validate an AI framework to enhance decision support and surgical planning for intracerebral hemorrhage treatment.

June 26, 2026Open Access

An agentic AI framework for integrated decision support and surgical planning in intracerebral hemorrhage

Puntos clave

Develop and validate an AI framework to enhance decision support and surgical planning for intracerebral hemorrhage treatment.
Retrospective analysis of 50 CT and CTA datasets from spontaneous intracerebral hemorrhage patients.
Implemented multi-class anatomical segmentation and volumetric quantification, followed by structured imaging biomarker encoding using JSON.
Used an automated trajectory modeling module for generating patient-specific surgical corridors.
AI-generated treatment recommendations showed 82% agreement with expert recommendations (Cohen's κ = 0.71).
In evacuation cases, AI generated feasible trajectories with a median angular deviation of 7.6° (IQR 5.1-9.8°) from expert-defined trajectories.
AI trajectories often provided equal or greater safety margins compared to expert planning.

Resumen

BACKGROUND: Intracerebral hemorrhage (ICH) remains associated with high mortality and treatment variability. Current workflows rely on fragmented imaging interpretation and operator-dependent surgical planning. METHODS: The objective was to develop and validate an agentic artificial intelligence (AI) framework integrating automated imaging analysis, guideline-based reasoning, and trajectory optimization for ICH treatment. Fifty consecutive computed tomography (CT) and computed tomography angiography (CTA) datasets from patients with spontaneous ICH were retrospectively analyzed. The system performed multi-class anatomical segmentation of skin, skull, brain, ventricles, and hematoma, followed by volumetric quantification and JavaScript Object Notation (JSON) based structured encoding of imaging biomarkers. A knowledge-based module incorporating international ICH guidelines generated risk stratification and treatment recommendations. When evacuation was indicated, an automated trajectory modeling module proposed a patient-specific minimally invasive surgical corridor. RESULTS: Overall agreement between AI-generated and expert treatment recommendations was 82% (41/50 cases), with substantial agreement beyond chance (Cohen's κ = 0.71). Discrepancies occurred primarily in borderline surgical indication scenarios. In evacuation candidates, the automated planner generated feasible trajectories in all 50 cases. Median angular deviation between AI-generated and expert-defined trajectories was 7.6°, interquartile range (IQR) 5.1-9.8°. AI-generated trajectories demonstrated equal or greater safety margins relative to expert planning in the majority of cases. End-to-end processing has a potential to substantially reduce simulated decision-support time compared with manual workflow. CONCLUSION: The proposed agentic AI framework enables structured, explainable, and workflow-integrated decision support for ICH management. This system may reduce operator variability and enhance precision in minimally invasive evacuation planning.

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