From Concept to Code: AI- Powered CODE-ICH Transforming Acute Neurocritical Response for Hemorrhagic Strokes

Background: Intracerebral hemorrhage (ICH) is among the most devastating forms of stroke, characterized by high early mortality and limited timesensitive treatment protocols compared to ischemic stroke. The absence of standardized emergency response frameworks and the shortcomings of conventional scoring systems highlight the urgent need for innovation in neurocritical care. Objective: This paper introduces and evaluates the CODEICH framework, along with two AIpowered tools HEADS-UP and SAHVAI designed to transform acute ICH management through realtime detection, volumetric analysis, and predictive modeling. Methods: We describe the development and implementation of HEADS-UP, a cloud-based AI system for early ICH detection in underserved populations, and SAHVAI, a convolutional neural networkbased tool for subarachnoid hemorrhage volume quantification. These tools were integrated into a novel paging and workflow system at a comprehensive stroke center to facilitate ultraearly intervention. Results: SAHVAI achieved 99.8% accuracy in volumetric analysis and provided 2D, 3D, and 4D visualization of hemorrhage progression. HEADS-UP enabled rapid triage and transfer, reducing reliance on subjective interpretation. Together, these tools operationalized the time is brain principle for hemorrhagic stroke and supported proactive, datadriven care in the neurointensive care unit (NICU). Conclusion: CODEICH, HEADS-UP, and SAHVAI represent a paradigm shift in hemorrhagic stroke care, delivering scalable, explainable, and multimodal AI solutions that enhance clinical decisionmaking, minimize delays, and promote equitable access to neurocritical care.