Resumo

RODRIGUEZ GONZALEZ, Eliany; MAR CORNELIO, Omar; GARCIA GARCIA, Arlety Leticia  e  BRON FONSECA, Barbara. Computational tools to support the diagnosis of patients with Parkinson's: a systematic review. RCCI [online]. 2023, vol.17, n.3  Epub 01-Set-2023. ISSN 2227-1899.

Artificial intelligence techniques as well as machine learning methods are used to automate the process of accurate pattern recognition. Given the need to study and obtain scalable diagnostic models that support the decision-making of medical professionals in the early and non-invasive detection of Parkinson's disease (PD), this research aims to: Identify the different computational tools that have been developed by researchers to support the diagnosis of patients with PD. A systematic review on computational tools to support the diagnosis of patients with PD was carried out. Searches were performed using Google Scholar, PubMed, Scopus, IEEE, Scielo, and Springer. A selection of potentially relevant articles that were associated with the topic under study was made. Speech and signal biomedical data sets were the most commonly used data types to develop and validate these models. However, magnetic resonance imaging and computed tomography were also used in the included studies. From this study it was possible to verify that most of the investigations are aimed at supporting the diagnosis of Parkinson's disease and the classification of stages or subtypes of PD. Although the models have great potential to improve the diagnosis and treatment of Parkinson's disease, further studies and clinical validations are still needed to determine their effectiveness and accuracy in different clinical settings.

Palavras-chave : Parkinson's disease; Medical diagnostic; artificial intelligence; machine learning.

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