Parkinson's Disease Motor Symptoms in Machine Learning: A Review

Ahlrichs, Claas; Lawo, Michael

doi:10.48550/arxiv.1312.3825

Cited by 2 publications

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“…The use of artificial intelligence (AI) has been reported in various studies to help medical science, Ahlrichs et al . [ 10 ] in a review study reported high efficiency of AI algorithms in the diagnosis of Parkinson’s disease. In a study by Bozkurt et al ., the authors used electrocardiography of 10 patients with obstructive sleep apnea (OSA) against 10 healthy controls.…”

Section: Introductionmentioning

confidence: 99%

Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID-19

Mohammadi¹,

Salari²,

Hosseinian-Far³

et al. 2023

Biology Methods and Protocols

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Background Artificial Intelligence (AI) as a suite of technologies can complement systematic review and meta-analysis studies and answer questions that cannot be typically answered using the traditional review protocols and reporting methods. The purpose of this protocol is to introduce a new protocol to complete systematic review and meta-analysis studies. Methods In this work, systematic review, meta-analysis, and meta-analysis network based on selected AI technique, and for p values less than 0.05 are followed, with a view to responding to questions and challenges that the global population are facing in light of the COVID-19 pandemic. Discussion Finally, it is expected that conducting reviews by following the proposed protocol can provide suitable answers to some of the research questions raised due to COVID-19.

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Section: Introductionmentioning

confidence: 99%

Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID-19

Mohammadi¹,

Salari²,

Hosseinian-Far³

et al. 2023

Biology Methods and Protocols

View full text Add to dashboard Cite

show abstract

“…Although the diagnosis of PD commonly relies on motor symptoms, nonmotor symptoms such as cognitive changes related to attention and other conditions have been studied as supportive means for early diagnosis (Postuma et al, 2015). A revolutionary substitute by several researchers studied the application of machine learning as measures for PD diagnosis and assessment with the help of analyzing motor symptoms, kinematics, and wearable sensor data (Ahlrichs and Lawo, 2013;Ramdhani et al, 2018;Belić et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation

et al. 2022

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Neurodegenerative Parkinson's Disease (PD) is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite (PGS) and intelligent Motor Assessment Tests (iMAT), produced within the i-PROGNOSIS European project (www.i-prognosis.eu), are explored in the current study. More specifically, data from 27 patients with PD at Stage 1 (9) and Stage 3 (18) produced from their interaction with PGS/iMAT are analyzed. Five feature vector (FV) scenarios are set, including features from PGS or iMAT scores or their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF), to infer the stage of each patient with PD. A Leave-One-Out Cross-Validation (LOOCV) method is adopted for testing the classification performance. The experimental results show that a high (>90%) classification accuracy is achieved from both data sources (PGS/iMAT), justifying the effectiveness of PGS/iMAT to efficiently reflect the motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions.

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Parkinson's Disease Motor Symptoms in Machine Learning: A Review

Cited by 2 publications

References 0 publications

Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID-19

Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID-19

Machine Learning-Based Analysis of Digital Movement Assessment and ExerGame Scores for Parkinson's Disease Severity Estimation

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