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This book introduces a groundbreaking AI-driven framework for rapid brain imaging assessment in acute neurological conditions such as stroke, traumatic injury, and hemorrhage. Combining deep learning, edge computing, and multimodal data fusion, ARIA-NET enables real-time decision support for emergency physicians and neurologists. The book explores the system's adaptive architecture, real-time optimization, and clinical workflow integration, offering both theoretical foundations and practical implementation strategies. Through case studies and algorithmic analysis, it highlights how intelligent imaging analytics can reduce diagnostic delay, enhance treatment precision, and ultimately improve patient survival rates. Designed for biomedical engineers, computer scientists, and clinicians, this volume bridges the gap between computational neuroscience and emergency medicine.