What is it about?

The paper systematically reviews the use of machine learning (ML) techniques for detecting and diagnosing cognitive impairment (CI) in Parkinson's Disease (PD). It evaluates various ML methods applied in published studies, including their feasibility, impacts, and performance across different data modalities like imaging, EEG, and speech. The review encompasses 70 studies and aims to provide recommendations for effective methods and outcomes in this field.

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Why is it important?

This review is crucial because cognitive impairment is a significant symptom of Parkinson's Disease, affecting quality of life and leading to conditions like Parkinson’s Disease Dementia (PDD). Traditional diagnostic methods are insufficient for early detection. With aging populations and shortages of neurologists, integrating machine learning can enhance diagnostic accuracy, allow for early intervention, and improve patient care. This work supports the development of non-invasive, efficient diagnostic tools, potentially transforming clinical practices for better management of cognitive decline in PD patients.


The paper highlights the versatility and potential of machine learning in diagnosing cognitive impairment in PD. It discusses the successful application of various ML techniques, including decision trees, support vector machines, and ensemble methods. The findings suggest that combining multiple data modalities often yields better diagnostic accuracy. Future research should focus on refining these methods, exploring new data modalities, and validating ML models in larger, diverse patient populations to ensure robust, clinically applicable diagnostic tools.

Callum Altham
Edge Hill University

Read the Original

This page is a summary of: Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review, PLoS ONE, May 2024, PLOS,
DOI: 10.1371/journal.pone.0303644.
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