Computational Approaches to Neurological Disorder Diagnosis: An In-Depth Review of Current Methods and Future...

Area of Science:

• Neurology
• Medical Informatics
• Computational Neuroscience

Background:

• Computational technologies are rapidly advancing medical diagnostics, especially for neurological disorders.
• Accurate and early diagnosis is crucial for managing conditions like Alzheimer's disease, Parkinson's disease, Epilepsy, Huntington's disease, and Amyotrophic Lateral Sclerosis.

Purpose of the Study:

• To comprehensively review computational approaches for diagnosing five major neurological disorders.
• To evaluate the efficacy, accuracy, and limitations of current diagnostic methods.
• To explore the potential of emerging technologies and multimodal data integration.

Main Methods:

• Systematic review of 140 peer-reviewed studies.
• Analysis of machine learning algorithms, neuroimaging techniques, and electrophysiological signal analysis.
• Evaluation of diagnostic methods for Alzheimer's disease, Parkinson's disease, Epilepsy, Huntington's disease, and ALS.

Main Results:

• Machine learning, neuroimaging, and signal analysis show significant potential in neurological disorder diagnosis.
• These computational methods are effective for early detection and differential diagnosis.
• Integration of multimodal data and advanced AI/deep learning can further enhance diagnostic accuracy.

Conclusions:

• Computational methodologies are transforming neurodiagnostics, offering improved precision and patient outcomes.
• Addressing challenges in clinical implementation is key for widespread adoption.
• Future research should focus on refining these techniques for better diagnostic capabilities.