Enhancing Stroke Recovery : Rehabilitation using MRI and Predictive modeling

Strokes frequently result in enduring challenges with movement and daily activities, emphasizing the importance of effective rehabilitation approaches. Our stroke rehabilitation model incorporates unique features that set it apart from existing software solutions, showcasing both functionality and innovation. By utilizing cuttingedge machine learning techniques, our system offers a more personalized and effective rehabilitation experience for stroke survivors. Unlike traditional methods, our application employs models for precise MRI image segmentation, which allows for detailed analysis and tailored treatment plans based on individual lesion characteristics. This level of customization ensures that each patient receives therapy that specifically targets their needs, enhancing recovery outcomes. The user-friendly interface is designed to be accessible for both medical professionals and patients, promoting seamless interaction and engagement. This work employs MRI scan datasets and clinical data in conjunction with predictive modelling techniques to enhance stroke recovery plans. By processing and analysing MRI scans and patient-specific clinical data, the proposed work aims to optimize rehabilitation protocols and improve treatment strategies. Predictive models are developed to forecast the likelihood of recovery and affected brain functions based on individual characteristics and imaging data. This integrated methodology provides data-driven insights, enhancing rehabilitation outcomes. Ultimately, the proposed work seeks to improve patient recovery trajectories and overall quality of life following stroke, offering hope for better outcomes and increased independence for individuals affected by this debilitating condition. Overall, the proposed work provides enhanced capabilities that empower users to achieve better recovery results, making it a truly innovative solution in the field of stroke rehabilitation.