Yelda Fırat, Meral Seferoğlu, Hakan Kılıçaslan, Ali Özhan Sıvacı, Murat Kaan Yılmaz, Yılmaz Kılıçaslan
The European Research Journal - 2025;11(6):1213-1226
Objectives: Multiple sclerosis (MS) is a chronic central nervous system disorder that causes demyelination, inflammation, and axonal damage, leading to permanent disabilities in motor, sensory, visual, and balance functions. This study aimed to develop an artificial intelligence (AI)-based, non-invasive diagnostic approach for MS detection using handwriting analysis, leveraging deep learning methods to identify disease-specific handwriting patterns. Methods: A classification model was designed using a convolutional neural network (CNN) based on the VGG16 architecture with transfer learning. The dataset consisted of 426 handwriting samples, including 213 from MS patients and 213 from healthy individuals. Data augmentation and early stopping techniques were employed to improve model generalization capability. Results: The proposed model achieved a validation accuracy of 83.72% and a test accuracy of 85%, indicating its robustness in distinguishing MS patients from healthy subjects. The confusion matrix analysis demonstrated a sensitivity of 86% and a specificity of 84%, indicating moderate discriminatory performance. Conclusions: The findings suggest that the developed AI-based model offers an effective, non-invasive diagnostic tool for MS detection. This approach provides a promising foundation for future research on monitoring disease progression and developing clinically applicable AI-supported diagnostic systems.
