A survey on computer-assisted Parkinson's Disease diagnosis

Autor(es)

Pereira, Clayton R. ^[1] ; Pereira, Danilo R. ^[2] ; Weber, Silke A. T. ^[3] ; Hook, Christian ^[4] ; de Albuquerque, Victor Hugo C. ^[5] ; Papa, Joao P. ^[6]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, Dept Comp, Sao Carlos, SP - Brazil

^[2] Univ Western Sao Paulo, Sao Paulo - Brazil

^[3] Sao Paulo State Univ, Botucatu Med Sch, Botucatu, SP - Brazil

^[4] Ostbayer Tech Hsch, Regensburg - Germany

^[5] Univ Fortaleza, Fortaleza, Ceara - Brazil

^[6] Sao Paulo State Univ, Sch Sci, Bauru - Brazil

• Tipo de documento: Artigo de Revisão
• Fonte: ARTIFICIAL INTELLIGENCE IN MEDICINE; v. 95, p. 48-63, APR 2019.
• Citações Web of Science: 7

Resumo

Background and objective: In this work, we present a systematic review concerning the recent enabling technologies as a tool to the diagnosis, treatment and better quality of life of patients diagnosed with Parkinson's Disease (PD), as well as an analysis of future trends on new approaches to this end. Methods: In this review, we compile a number of works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer and Hindawi Publishing Corporation. Each selected work has been carefully analyzed in order to identify its objective, methodology and results. Results: The review showed the majority of works make use of signal-based data, which are often acquired by means of sensors. Also, we have observed the increasing number of works that employ virtual reality and e-health monitoring systems to increase the life quality of PD patients. Despite the different approaches found in the literature, almost all of them make use of some sort of machine learning mechanism to aid the automatic PD diagnosis. Conclusions: The main focus of this survey is to consider computer-assisted diagnosis, and how effective they can be when handling the problem of PD identification. Also, the main contribution of this review is to consider very recent works only, mainly from 2015 and 2016. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular