# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 2 Literature Review
### Stroke Diagnosis Methods  
Traditional stroke diagnosis predominantly employs imaging techniques, notably computed tomography (CT) scans, which are vital for swiftly detecting acute ischemic strokes (AIS) and excluding hemorrhagic strokes [21]. CT-angiography (CTA) is essential for detecting large-vessel occlusions (LVOs) and evaluating salvageable brain tissue via multiphase CTA and CT-perfusion (CTP) [22]. Magnetic resonance imaging (MRI) is utilized for patients beyond the thrombolysis time window, aiding in treatment selection based on tissue viability rather than time since stroke onset [23]. The imaging methods' primary objectives include rapid hemorrhagic stroke exclusion, LVO identification, and ischemic core size estimation to inform treatment decisions [24]. Despite their efficacy, traditional methods are often time-intensive and susceptible to human error, prompting the creation of computer-aided diagnostic (CAD) systems that improve diagnostic precision and efficiency through advanced image analysis [25]. Furthermore, metabolomics is emerging as a valuable early diagnostic tool by detecting ischemic stroke biomarkers, although its clinical application remains limited [26]. Collectively, these diagnostic approaches aim to enhance patient outcomes through timely and precise stroke diagnosis [27]. In clinical practice, stroke assessment commences with a targeted history and neurological examination, often measured by the NIH Stroke Scale (NIHSS). Patients receive an evaluation of awareness, facial symmetry, motor strength, coordination, and linguistic function, in addition to vital signs and fundamental laboratory tests (full blood count, electrolytes, coagulation studies, and blood glucose). This preliminary assessment identifies candidates for time-sensitive therapies and aids in differentiating stroke mimics. Subsequent to clinical screening, imaging is conducted promptly. A non-contrast head CT scan is performed to rule out cerebral bleeding. If ischemia is suspected, CT angiography (CTA) and CT perfusion (CTP) tests are performed to identify major artery occlusions and evaluate the at-risk penumbral tissue. In specific instances--especially when the precise initiation time is ambiguous--a diffusion-weighted MRI sequence offers enhanced sensitivity for detecting early ischemia alterations. Novel blood biomarkers(e.g., D dimer, glial fibrillary acidic protein) may enhance diagnostic precision, but are still under development. AI-based diagnostic methods exhibit varied effectiveness relative to traditional techniques across medical disciplines [28]. In gastric neoplasia endoscopic diagnosis, AI systems achieved comparable accuracy to human endoscopists, particularly excelling in high-grade dysplastic lesion detection with an 80% detection rate versus 29.1% by specialists [29]. Moreover, AI systems significantly expedited diagnostic processes, indicating potential benefits for human endoscopic diagnosis [30]. In chest X-ray evaluations, AI-assisted diagnostics improved nonradiologist physicians' accuracy in identifying and localizing lung lesions, achieving an area under the curve (AUC) of 0.84 compared to 0.71 without AI support [31]. This illustrates AI's capability to enhance diagnostic precision and efficiency, albeit its influence on clinical decision making remains ambiguous [32]. In dementia diagnosis, AI combined with MRI has elevated the accuracy for conditions like Alzheimer's disease, reporting rates between 73.3% and 99%, highlighting AI's potential to refine an early and accurate diagnosis alongside conventional imaging [33]. Additionally, AI models in gastrointestinal diagnostics have shown high sensitivity and specificity, with deep learning and ensemble methods, achieving sensitivities of 89.8% and 95.4%, respectively, and specificities around 91.9% and 90.9% [34]. This indicates that AI may equal or exceed traditional methods in specific scenarios, though further large-scale studies are essential for validation across various clinical contexts [35].