AI-Driven Stroke Classification and Detection: A Retrospective Study Using MRI Imaging - Neshat, Nilofer; Gupta, Shubham; Unadkat, Nency; Jain, Khush; Rathwa, Anil (Journal of Neonatal Surgery, 2025)
Purpose: This study aims to develop an artificial intelligence (AI)-based method for accurately distinguishing between acute and chronic stroke using MRI imaging, to enhance diagnostic precision, treatment planning, and prognosis evaluation. Method: A total of 700 patient MRI datasets were utilized, with an 80/20 split for training and testing. The MRI images underwent preprocessing and key feature extraction, followed by classification using a Support Vector Machine (SVM). Model performance was evaluated using standard metrics: precision, recall, F1-score, and support. Results: The training set, comprising approximately 2,480 data points, demonstrated strong model performance. For non-stroke cases, the model achieved a precision of 0.95 and a recall of 0.92, indicating a low false positive rate. For stroke cases, the precision was 0.95 and the recall was 0.88, reflecting a slightly higher false positive rate. High F1-scores in both categories confirmed a well-balanced performance between precision and recall. Conclusion: The proposed AI-based classification model effectively distinguishes between stroke and non-stroke MRI images, showing promise in aiding clinical diagnosis and improving patient outcomes. Future research will focus on addressing dataset imbalances and evaluating the performance of alternative machine learning algorithms to further enhance model robustness