Recent Advances in Artificial Intelligence Paradigms for the Detection and Diagnosis of Neurological Diseases

Dear Colleagues,

Artificial intelligence (AI) has provided revolutionary tools for the early detection and diagnosis of neurological diseases and, by analyzing vast amounts of medical data, including neuroimaging, genetic information, and clinical records, AI algorithms can identify subtle patterns and biomarkers indicative of neurological disorders at their earliest stages. For instance, deep learning models have demonstrated remarkable accuracy in detecting the early signs of Alzheimer’s disease from MRI scans, sometimes even before symptoms become clinically apparent. AI can also process and interpret complex data from sources like EEG signals, helping to diagnose conditions such as epilepsy more efficiently. These technological advancements not only enhance diagnostic accuracy but also significantly reduce the time required for diagnosis, enabling earlier intervention and more effective treatment plans. Moreover, AI paradigms can provide personalized risk assessments and predictions, aiding in the management and monitoring of disease progression. The integration of AI into neurological diagnostics not only enhances diagnostic precision but also streamlines workflows, potentially reducing healthcare costs and improving access to quality care in resource-limited settings.

This Special Issue aims to gather together original contributions or critical reviews that focus on artificial intelligence models and machine learning algorithms for the early detection, computer-aided diagnosis, and progression monitoring of neurological diseases.

Topics of interest include, but are not restricted to, the following:

• The early detection of symptoms in Alzheimer’s disease;
• EEG, MEG signal, and MR image analysis for neurological diseases;
• AI-driven biomarker discovery in neurodegenerative diseases;
• Multimodal data fusion for the early detection of neurological diseases;
• Predictive analytics for neurological disease progression;
• Human motion and behavior analysis for the diagnosis of Parkinson’s disease and Huntington’s disease;
• The detection and assessment of phonatory disorders in neurological disease;
• Artificial intelligence and machine learning algorithms for the diagnosis of neurological symptoms.

Dr. Yunfeng Wu
Guest Editor

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• biomedical signal processing
• artificial intelligence
• data mining