Application of deep learning models for automated identification of Parkinson’s disease: a review (2011–2021)

Loh, Hui Wen and Hong, Wanrong and Ooi, Chui Ping and Chakraborty, Subrata and Barua, Prabal Datta ORCID: https://orcid.org/0000-0001-5117-8333 and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Soar, Jeffrey ORCID: https://orcid.org/0000-0002-4964-7556 and Palmer, Elizabeth E. and Acharya, U. Rajendra (2021) Application of deep learning models for automated identification of Parkinson’s disease: a review (2011–2021). Sensors, 21:7034. pp. 1-27.

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Abstract

Abstract: Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright: c 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0).
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 25 Oct 2021 05:27
Last Modified: 10 Nov 2021 02:04
Uncontrolled Keywords: Parkinson’s disease (PD); deep learning; computer-aided diagnosis (CAD); SPECT; PET; MRI; EEG; gait; handwriting; speech
Fields of Research (2008): 08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460999 Information systems not elsewhere classified
Socio-Economic Objectives (2008): C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: https://doi.org/10.3390/s21217034
URI: http://eprints.usq.edu.au/id/eprint/43984

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