Editorial: AI and Machine Learning Application for Neurological Disorders and Diagnosis

Kandala N. V. P. S. Rajesh ^1* Venkata Mani Vakamulla ² Sunil Kumar T ³

• ¹ VIT-AP University, Amaravati, India
• ² National Institute of Technology Warangal, Warangal, Telangana, India
• ³ University of Gävle, Gävle, Sweden

Neurological disorders disrupt the normal functioning of the nervous system, which includes the central nervous system and the peripheral nervous system [1]. These disorders encompass a wide range of conditions, such as, epilepsy and seizures, dysarthric speech, schizophrenia, attention deficit hyperactivity disorder (ADHD), Parkinson's disease, brain strokes, and Alzheimer's disease, among others [2].The causes of these disorders vary and can include genetic mutations, congenital abnormalities, infections, or injuries to the nervous system. Diagnosing these conditions often involves various tests, such as electroencephalography (EEG), electromyography (EMG), nerve conduction studies, imaging tests, and sleep studies.While these diagnostic tools are essential, manually analyzing the results over extended periods can be prone to errors and is both time-consuming and labor-intensive. To address this challenge, computeraided diagnostic (CAD) systems are increasingly being developed using machine learning (ML) models. These technologies can assist clinicians by providing reliable second opinions, reducing the workload, and improving efficiency by significantly reducing the time required for diagnosis [3]. This research topic focuses on advancing methodologies for identifying various neurological disorders by applying artificial intelligence (AI) and ML techniques.