A survey on computer-assisted Parkinson's Disease diagnosis

Author(s)

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]

Affiliation

^[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

• Document type: Review article
• Source: ARTIFICIAL INTELLIGENCE IN MEDICINE; v. 95, p. 48-63, APR 2019.
• Web of Science Citations: 7

Abstract

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)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants