AI to Detect Stroke-Related Facial Weakness

Stroke is a serious medical condition caused by the interruption or reduction of blood flow to the brain, often resulting in facial weakness or disability. Early detection of stroke symptoms, particularly facial asymmetry, enables timely medical intervention and improves patient outcomes. This paper proposes an artificial intelligence-based approach that utilizes a limited set of neutral and smiling facial images to detect stroke-related facial weakness. To address data scarcity, a hybrid dataset is developed by combining real images with synthetic data generated using FaceGAN and deepfake-based augmentation techniques. Facial regions are segmented into left and right halves using landmark detection, followed by affine transformations based on Delaunay triangulation for geometric alignment. A deep learning architecture integrating a ConvNeXt encoder with a lightweight convolutional neural network (CNN) decoder is employed for feature extraction and classification. The model demonstrates strong robustness and achieves an accuracy of 98.9% using four- fold cross-validation. The results highlight the potential of AI- assisted facial analysis for rapid and accessible stroke screening in clinical and resource-limited environments.