Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders
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.
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ML algorithms have been instrumental in monitoring and managing chronic diseases like Parkinson's and multiple sclerosis.
“Moreover, ML algorithms have been instrumental in monitoring and managing chronic diseases, like Parkinson’s and multiple sclerosis [187,188].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [188]
These innovative devices can continuously track various health parameters, giving users personalized insights and feedback.
“These innovative devices can continuously track various health parameters, giving users personalized insights and feedback [6,12,13,16,188].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [188]
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- Authors
- Raghavendra, U., Acharya, U. R., & Adeli, H.
- Affiliations
- Manipal Academy of Higher Education (IN)
- International Research Organization for Advanced Science and Technology (JP)
- The Ohio State University (US)
- Venue
- European Neurology
- Publisher
- S. Karger AG
- Published
- 2019
- Volume
- 82
- Issue
- 1-3
- Pages
- 41-64
- ISSN
- 0014-3022 (ISSN-L)
- 1421-9913
- OpenAlex
- W2991524019
- Cited by
- 174
- References
- 299
- Citations by year
- 2026: 14 · 2025: 28 · 2024: 34 · 2023: 27 · 2022: 29 · 2021: 27 · 2020: 15
- FWCI
- 19.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Neurology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- computer-aided diagnosis, neurological disorders, epilepsy, Parkinson's disease, Alzheimer's disease, multiple sclerosis, ischemic stroke, physiological signals, medical images, feature extraction, dimensionality reduction, feature selection, classification, clinical decision support
- MeSH terms
- Artificial Intelligence, Central Nervous System Diseases, Diagnosis, Computer-Assisted, Humans, Signal Processing, Computer-Assisted
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