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Review
. 2023 Sep 20;13(4):591-612.
doi: 10.1007/s13534-023-00319-2. eCollection 2023 Nov.

A review of emergent intelligent systems for the detection of Parkinson's disease

Samiappan Dhanalakshmi  1 , Ramesh Sai Maanasaa  1 , Ramesh Sai Maalikaa  1 , Ramalingam Senthil  2
Affiliations
Review

A review of emergent intelligent systems for the detection of Parkinson's disease

Samiappan Dhanalakshmi et al. Biomed Eng Lett. .

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.

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Conflict of interest statement

Competing interestsThe authors declare that there is no potential conflict of interest.

Figures

Fig. 1
Fig. 1
PD symptoms are classified into motor and non-motor symptoms
Fig. 2
Fig. 2
PRISMA model
Fig. 3
Fig. 3
Classification of PD detection analyzed by various studies in this review
Fig. 4
Fig. 4
Distribution of literature selected in this review
Fig. 5
Fig. 5
Shows the SPECT scan images for a HC, b PD patient [20]
Fig. 6
Fig. 6
SPECT scans that achieved more than 90% accuracy
Fig. 7
Fig. 7
General CNN architecture
Fig. 8
Fig. 8
Best accuracy obtained using EEG recording
Fig. 9
Fig. 9
Depicts sound production in person [48]
Fig. 10
Fig. 10
Handwriting tasks showing spiral task for a HC, b PD. Meander task for c HC, d PD [75]
Fig. 11
Fig. 11
Best Accuracy obtained using handwriting dynamics
Fig. 12
Fig. 12
Proposed architecture to diagnose Parkinson disease by [89]
Fig. 13
Fig. 13
Accuracy obtained using gait
Fig. 14
Fig. 14
CAD Implementation
See this image and copyright information in PMC

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