# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 3 Methodological Framework
### Integration of Digital Twin and Telemedicine Approaches  
Recent advancements in digital twin frameworks and telemedicine services have demonstrated their potential to revolutionize healthcare delivery. For example, a review of AI-driven digital twin frameworks for cardiovascular disease diagnosis and management has provided valuable insights into leveraging these technologies for clinical decision support [52]. In addition, the development of a convolutional neural network-based digital diagnostic tool for the identification of psychosomatic illnesses highlights the transformative role of deep learning in diagnostics [53]. The comprehensive literature on digital twin technology in healthcare further underscores its importance in achieving system-wide integration and personalized patient care [54]. Moreover, studies on centralized-healthcare cyber-physical system architectures [55] and on the challenges and solutions for resilient telemedicine services [56] emphasize the necessity for robust, scalable system designs in clinical settings. These works collectively inform our methodology by emphasizing the integration of multi-modal data, advanced calibration techniques, and scalable digital frameworks--principles that are integral to our AI-powered stroke diagnosis system. Although our methodology is guided by principles from the digital twin literature--such as multi-modal data fusion, model calibration, and integration with clinical decision support--it is crucial to highlight that our present implementation does not represent a fully developed digital twin. A genuine digital twin is defined as a real-time, bi-directional simulation of patient physiology, integrating instantaneous sensor feedback, ongoing model synchronization, and closed-loop optimization of clinical interventions. Conversely, our system functions as a static diagnostic module: it amalgamates retrospective structured and imaging data, produces calibrated predictions, and facilitates early stroke categorization. Consequently, we characterize this work as a replicable, data-driven diagnostic framework instead of a digital twin, while acknowledging that future advancements may evolve our system into dynamic, simulation-based digital twin structures. In conclusion, Section 3 lays the groundwork for the methodological framework that amalgamates multi-modal data integration, cutting-edge modeling techniques, and rigorous calibration strategies, all of which are essential components of our research design. As illustrated in Figure 1, our system integrates structured clinical data and curated imaging data through distinct preprocessing pipelines. The clinical data are processed via a multi-layer perceptron (MLP), while the imaging data are analyzed using a MobileNet-based convolutional neural network. These features are then fused and passed through ensemble methods and a calibration module, which together yield the final stroke classification. Figure 1: Overall system architecture diagram for the AI-powered stroke diagnosis system. This diagram provides an overview of the system pipeline. Two primary data streams are shown: Structured Clinical Data from the eICU database and Curated Imaging Data. Each stream undergoes dedicated preprocessing steps--data cleaning, normalization, and encoding for clinical data; and resizing, normalization, and augmentation for imaging data. The processed data are then fed into feature extraction models (an MLP for clinical data and a MobileNet-based CNN for imaging data). The resulting feature vectors are fused, and ensemble methods (soft-voting and stacking) are applied to enhance the prediction accuracy. Finally, a calibration and threshold optimization module refines the output to achieve reliable stroke classification.