Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders

Eur Neurol. 2019;82(1-3):41-64. doi: 10.1159/000504292. Epub 2019 Nov 19.

Abstract

Background: Authors have been advocating the research ideology that a computer-aided diagnosis (CAD) system trained using lots of patient data and physiological signals and images based on adroit integration of advanced signal processing and artificial intelligence (AI)/machine learning techniques in an automated fashion can assist neurologists, neurosurgeons, radiologists, and other medical providers to make better clinical decisions.

Summary: This paper presents a state-of-the-art review of research on automated diagnosis of 5 neurological disorders in the past 2 decades using AI techniques: epilepsy, Parkinson's disease, Alzheimer's disease, multiple sclerosis, and ischemic brain stroke using physiological signals and images. Recent research articles on different feature extraction methods, dimensionality reduction techniques, feature selection, and classification techniques are reviewed. Key Message: CAD systems using AI and advanced signal processing techniques can assist clinicians in analyzing and interpreting physiological signals and images more effectively.

Keywords: Classification algorithm; Computer-aided diagnosis; Machine learning; Neurological disorder.

Publication types

  • Review

MeSH terms

  • Artificial Intelligence*
  • Central Nervous System Diseases / diagnosis*
  • Diagnosis, Computer-Assisted / methods*
  • Humans
  • Signal Processing, Computer-Assisted