Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

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 ye...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Hui Wen Loh, Wanrong Hong, Chui Ping Ooi, Subrata Chakraborty, Prabal Datta Barua, Ravinesh C. Deo, Jeffrey Soar, Elizabeth E. Palmer, U. Rajendra Acharya
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Parkinson’s disease (PD) deep learning computer-aided diagnosis (CAD) SPECT PET MRI Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/5b74bde7b8b4494c81f17c469202c55b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5b74bde7b8b4494c81f17c469202c55b
record_format	dspace
spelling	oai:doaj.org-article:5b74bde7b8b4494c81f17c469202c55b2021-11-11T19:03:56ZApplication of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)10.3390/s212170341424-8220https://doaj.org/article/5b74bde7b8b4494c81f17c469202c55b2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7034https://doaj.org/toc/1424-8220Parkinson’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.Hui Wen LohWanrong HongChui Ping OoiSubrata ChakrabortyPrabal Datta BaruaRavinesh C. DeoJeffrey SoarElizabeth E. PalmerU. Rajendra AcharyaMDPI AGarticleParkinson’s disease (PD)deep learningcomputer-aided diagnosis (CAD)SPECTPETMRIChemical technologyTP1-1185ENSensors, Vol 21, Iss 7034, p 7034 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Parkinson’s disease (PD) deep learning computer-aided diagnosis (CAD) SPECT PET MRI Chemical technology TP1-1185
spellingShingle	Parkinson’s disease (PD) deep learning computer-aided diagnosis (CAD) SPECT PET MRI Chemical technology TP1-1185 Hui Wen Loh Wanrong Hong Chui Ping Ooi Subrata Chakraborty Prabal Datta Barua Ravinesh C. Deo Jeffrey Soar Elizabeth E. Palmer U. Rajendra Acharya Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)
description	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.
format	article
author	Hui Wen Loh Wanrong Hong Chui Ping Ooi Subrata Chakraborty Prabal Datta Barua Ravinesh C. Deo Jeffrey Soar Elizabeth E. Palmer U. Rajendra Acharya
author_facet	Hui Wen Loh Wanrong Hong Chui Ping Ooi Subrata Chakraborty Prabal Datta Barua Ravinesh C. Deo Jeffrey Soar Elizabeth E. Palmer U. Rajendra Acharya
author_sort	Hui Wen Loh
title	Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)
title_short	Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)
title_full	Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)
title_fullStr	Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)
title_full_unstemmed	Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)
title_sort	application of deep learning models for automated identification of parkinson’s disease: a review (2011–2021)
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5b74bde7b8b4494c81f17c469202c55b
work_keys_str_mv	AT huiwenloh applicationofdeeplearningmodelsforautomatedidentificationofparkinsonsdiseaseareview20112021 AT wanronghong applicationofdeeplearningmodelsforautomatedidentificationofparkinsonsdiseaseareview20112021 AT chuipingooi applicationofdeeplearningmodelsforautomatedidentificationofparkinsonsdiseaseareview20112021 AT subratachakraborty applicationofdeeplearningmodelsforautomatedidentificationofparkinsonsdiseaseareview20112021 AT prabaldattabarua applicationofdeeplearningmodelsforautomatedidentificationofparkinsonsdiseaseareview20112021 AT ravineshcdeo applicationofdeeplearningmodelsforautomatedidentificationofparkinsonsdiseaseareview20112021 AT jeffreysoar applicationofdeeplearningmodelsforautomatedidentificationofparkinsonsdiseaseareview20112021 AT elizabethepalmer applicationofdeeplearningmodelsforautomatedidentificationofparkinsonsdiseaseareview20112021 AT urajendraacharya applicationofdeeplearningmodelsforautomatedidentificationofparkinsonsdiseaseareview20112021
_version_	1718431647126257664

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Ejemplares similares