Hybrid Artificial Intelligence Model for Automated Epilepsy Diagnosis Using Diverse Data Modalities

Abstract

Epilepsy diagnosis remains a complex clinical challenge due to the heterogeneous nature of seizure manifestations, neurological variations, and limitations associated with conventional diagnostic approaches. Recent advances in artificial intelligence (AI) have created opportunities for developing automated systems capable of integrating diverse biomedical data modalities, including magnetic resonance imaging (MRI), neuroanatomical features, functional connectivity patterns, and clinical characteristics. This research presents a conceptual hybrid artificial intelligence model for automated epilepsy diagnosis by combining multimodal feature extraction, deep learning architectures, and machine learning-based classification strategies. The proposed approach addresses limitations of single-modality diagnostic systems by enabling comprehensive analysis of structural and functional brain information. Previous studies demonstrated the effectiveness of machine learning and deep learning approaches in identifying epilepsy-related biomarkers, differentiating neurological disorders, and predicting surgical outcomes (Akrami et al., 2022; Chang et al., 2023). Furthermore, investigations into network phenotypes and graph-based neurological patterns highlight the importance of integrating complex brain connectivity information for improved diagnostic accuracy (Garcia-Ramos et al., 2022). The proposed hybrid framework emphasizes data fusion, feature optimization, and interpretable prediction mechanisms to support clinical decision-making. The analysis indicates that AI-assisted multimodal models have significant potential for improving epilepsy detection, personalized treatment planning, and neurological assessment. However, challenges related to dataset variability, model generalization, and clinical implementation remain important considerations for future research.