Diagnosis and monitoring of patients with Parkinson's disease with the use of Smartphone
Keywords:
Parkinson, inteligencia artificial, aprendizaje automático, bradikinesia, temblor en reposo, rigidez muscular, inestabilidad postural, vozAbstract
Introduction: currently, many efforts are being made to achieve accurate medical diagnoses and subsequent patient follow-up. One of the diseases that has been the subject of research for this purpose is Parkinson's disease. Particularly in the area of artificial intelligence, and more specifically machine learning, several results have been shown to corroborate this.
Objective: to find evidence supporting the use of smartphones for data extraction and the main metrics and algorithms used to classify patients with this disease.
Methods: the PRISMA (2020) checklist was used to conduct the search. The search covered the last 6 years and included articles written in Spanish or English. Studies were selected from sources such as Scopus, PubMed, SciElo, IEEE, Google Scholar, and ACM. The last search was conducted in January 2025. To avoid review bias, only previously published articles were accepted, as well as those that could be viewed. Tools such as Mendeley, Excel, and descriptive statistics were used to synthesize the results.
Development: 72 articles were obtained that met the criteria for review, 20 of which used cell phones for data extraction and classification.
Conclusions: there is evidence supporting the use of smartphones for the classification of patients with Parkinson's disease. Among the most commonly used metrics are gait, tremor, bradykinesia, and in recent years, voice has been incorporated as an early assessment measure for this disease.
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