# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 1 Introduction  
Stroke continues to be one of the foremost contributors to both morbidity and mortality at a global level, posing considerable diagnostic challenges attributable to its complex pathogenesis and the diverse imaging modalities required for its accurate identification [1]. Recent progressions in machine learning (ML) and artificial intelligence (AI) have initiated a transformative shift in this field by enhancing the diagnostic accuracy and operational efficiency [2]. These advanced technologies possess the capability to analyze heterogeneous data--from structured clinical indicators available in extensive repositories such as the eICU Collaborative Research Database (eICU DB) [3] to intricate imaging data derived from CT and MRI scans--thereby reducing diagnostic times and promoting timely, evidence-based clinical decisions [4]. AI-augmented models, particularly deep learning architectures such as convolutional neural networks (CNNs), have exhibited substantial improvements in the evaluation of imaging data [5]. Reported detection accuracies are documented to range from 70% to 90% with MRI images, and can ascend to as high as 98.42% for CT images when employing cutting-edge models [6]. Moreover, these systems demonstrate adeptness in differentiating between ischemic and hemorrhagic strokes while optimizing patient selection for interventions like thrombolysis [7]. Nonetheless, challenges persist regarding the data quality, class imbalance, algorithmic transparency, and the seamless incorporation of these technologies into clinical workflows [8]. Additionally, the inherent variability in stroke manifestations and imaging techniques necessitates the implementation of robust, multimodal strategies [9]. The research articulated in this paper addresses these challenges through the formulation of an integrated stroke diagnosis framework that capitalizes on both structured clinical data from the eICU DB and curated imaging data [10]. By synthesizing traditional ML approaches--such as XGBoost and CatBoost--with contemporary deep learning methodologies, exemplified by a MobileNet-based CNN, and employing ensemble techniques, our strategy aspires to elevate both the diagnostic accuracy and clinical applicability [11]. This paper is systematically organized into seven chapters. Section 1, the Introduction, elucidates the significance of stroke diagnosis and the transformative capabilities of AI, summarizing the challenges and prospects inherent in multimodal data integration while delineating the paper structure. Section 2, the Literature Review, systematically explores sophisticated AI diagnostic methodologies within the healthcare domain, focusing on stroke detection through both conventional and novel ML techniques, and contrasts various models to illuminate the progression of diagnostic practices. Section 3, the Methodological Framework, elaborates on the theoretical underpinnings of this study, detailing the amalgamation of structured and imaging data, preprocessing strategies for diverse datasets, and the rationale underlying the selected machine learning algorithms. Section 4, the Methodology Development, builds upon the theoretical framework by articulating the experimental design, encompassing the technological components, multimodal data integration strategies, modeling techniques, and optimization processes employed. Section 5, the Theoretical Evaluation, presents a rigorous critique of the models utilizing quantitative metrics and visual diagnostics, discussing the trade-offs among various approaches and evaluating the support for the foundational hypotheses. Section 6, the Discussion, synthesizes the findings, addressing the strengths and limitations of the approach, the representativeness of the data, model calibration, and proposing future research trajectories for system enhancement.