AI-Driven Multimodal Prognosis in Stroke: Integrating Neuroimaging and Longitudinal Clinical Data for Enhanced Detection and Long-Term Outcome Prediction

Stroke is an endemic cause of morbidity and mortality worldwide with respect to improvement of patient outcome necessitating earlier diagnosis and effective treatment. To address this clinical scenario, here, we advance an AI-based innovative framework where sophisticated neuroimaging techniques were assimilated and linked with clinical longitudinal data aimed to detect classify, and make predictions about these critical stroke types of hemorrhage, ischemic, and TIA. It involves two streams: the imaging stream works with the CT/MRI scans through preprocessing and augmentation, then segmentation using DR-UNet, and finally detection using YOLOv4 in terms of giving an output for strong imaging features. The clinical streams process patient-specific data, such as demographics and medical history, including vital signs through dedicated feature extraction. The two feature sets are then combined-through concatenation and additional joint learning and modeled sequentially by an LSTM network to capture the temporal dependencies along the treatment trajectory. The comprehensive analysis of state-of-the-art methods, including DR-UNet for segmentation, YOLOv4 for detection and localization, and LSTM for prognosis, shows that each individual approach is highly performing. These results imply that our proposed framework, integrating these techniques into a unified multimodal pipeline, is theoretically optimized to enhance diagnostic accuracy and deliver personalized prognostic predictions. We then proceed to discuss and outline the future research directions to be taken into this theoretical framework in terms of its translation to practical applications, including its integration into clinical workflows and interfacing with electronic health record systems. This will thus enable real-world validation of our method and open the way toward improved patient management and advanced treatment strategies.