International Journal of Science, Engineering and TechnologyAuthors: Sachin Phuldev Yadav, Professor Bhosale S.B, Professor Bangar A.P, Professor A.A. Khatri
Abstract: Neurological emergencies span a wide spectrum, from sudden vascular events such as ischemic stroke to acute psychiatric-neurological crises such as panic attacks. Both categories are time-sensitive and both currently rely heavily on manual clinical assessment, which limits the speed and consistency of detection. This paper presents a detailed review of six recent peer-reviewed sources (2024–2025) on artificial intelligence (AI) in neurology, and uses their combined findings to build a case for, and a design of, an integrated two-module early-detection framework: an imaging-based deep-learning pipeline for stroke triage, and a wearable-sensor-based sequence-modelling pipeline for panic/anxiety detection. The review covers foundational AI concepts (machine learning, deep learning, neural network architectures), the current clinical evidence base for stroke-imaging AI, the comparatively nascent evidence for physiological/behavioural AI in affective and psychiatric-neurological states, and the shared ethical, technical, and regulatory challenges — interpretability, bias, data privacy, and validation — that recur across every source reviewed. The paper then details a proposed system architecture, dataset and preprocessing requirements, candidate model architectures, an evaluation plan with specific metrics, expected outcomes, and a discussion of limitations and future work. The overall conclusion is that stroke-detection AI is a mature and clinically validated field that can serve as a design template for a still-emerging class of wearable, sensor-driven AI tools for detecting acute panic and anxiety states.
