NeuroFusion: An Intelligent Multimodal AI Framework for Early Diagnosis and Risk Stratification of Neurological Disorders

Abstract

The growing worldwide incidence of neurological conditions, including Alzheimer disease, Parkinson disease, stroke, epilepsy, and multiple sclerosis, requires the development of reliable diagnostic systems with early-stage multimodal sensitivity of neuroimaging, neuro-signals and clinical text. The proposed study is a smart multimodal AI system, NeuroFusion that fuses MRI, CT, PET, EEG, fMRI, and Electronic Health Records (EHR) with cross-attention transformers and deep representation learning. The architecture has included superior preprocessing pipelines, feature compression using autoencoders, multimodal encoders, and a NeuroFusion cross-attention layer to align heterogeneous features. The system was tested on a variety of neurological data obtained in Kaggle and open medical repositories. The experimental results indicate impressive performance with a 98.7 % classification accuracy, 96.2 % Dice score to the lesion segmentation, 96.5 % temporal consistency to the EEG/fMRI-based progression patterns, and 95.3 % risk prediction with survival-based stratification. The comparative analysis proved that NeuroFusion is better than the traditional CNNs, unimodal deep networks, hybrid fusion models and time-series Transformers. This was further improved by the clinical text integration by BioBERT, which increased the stability and interpretability of the prediction by extracting the symptoms, temporal events and comorbidities. The results indicate that NeuroFusion can successfully identify the complementary patterns on structural, functional, and contextual modalities and provide trustworthy decision support services to neurologists. It has a great potential in real-time clinical implementation, especially with early diagnosis, tailored treatment design, and chronic follow-up of complicated neurological conditions, which is suggested by its proposed architecture.

Ähnliche Arbeiten