A review of emergent intelligent systems for the detection of Parkinson's disease
- PMID: 37872986
- PMCID: PMC10590348
- DOI: 10.1007/s13534-023-00319-2
A review of emergent intelligent systems for the detection of Parkinson's disease
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder affecting people worldwide. The PD symptoms are divided into motor and non-motor symptoms. Detection of PD is very crucial and essential. Such challenges can be overcome by applying artificial intelligence to diagnose PD. Many studies have also proposed the implementation of computer-aided diagnosis for the detection of PD. This systematic review comprehensively analyzed all appropriate algorithms for detecting and assessing PD based on the literature from 2012 to 2023 which are conducted as per PRISMA model. This review focused on motor symptoms, namely handwriting dynamics, voice impairments and gait, multimodal features, and brain observation using single photon emission computed tomography, magnetic resonance and electroencephalogram signals. The significant challenges are critically analyzed, and appropriate recommendations are provided. The critical discussion of this review article can be helpful in today's PD community in such a way that it allows clinicians to provide proper treatment and timely medication.
Keywords: Intelligent algorithms; Machine learning; Neural networks; Neurodegenerative disorder; Parkinson’s disease.
© Korean Society of Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Conflict of interest statement
Competing interestsThe authors declare that there is no potential conflict of interest.
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