Neuro – Beta

Neurology

Multimodal Machine Learning for Diagnosis of Multiple Sclerosis Using Optical Coherence Tomography in Pediatric Cases

Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data

Privacy-preserving dementia classification from EEG via hybrid-fusion EEGNetv4 and federated learning

Advancing brain age modeling for individuals with multiple sclerosis

Accurate early detection of Parkinson’s disease from SPECT imaging through Convolutional Neural Networks

Parkinson’s image detection and classification based on deep learning

Automated Whole-Brain Focal Cortical Dysplasia Detection Using MR Fingerprinting With Deep Learning

Longitudinal Risk Prediction for Pediatric Glioma with Temporal Deep Learning

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia

External validation of automated focal cortical dysplasia detection using morphometric analysis

Machine learning to investigate superficial white matter integrity in early multiple sclerosis

AI-based model for automatic identification of multiple sclerosis based on enhanced sea-horse optimizer and MRI scans