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Applications of machine learning for computer-aided diagnosis of Parkinson’s disease: progress and benchmark case study

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Comprehensive review highlights machine learning's role in Parkinson's disease diagnosis, suggesting improvements in clinical practice.

Machine learning shows potential in enhancing the diagnosis of Parkinson's disease through various data modalities.
Among the 117 articles reviewed, only 28 demonstrated a low risk of bias, indicating a need for improved study designs.
The analysis covered diverse data types—such as voice and neuroimaging—with voice data being the most prevalent at 40.2%.
Current limitations of applying machine learning in clinical settings were discussed, highlighting areas for future research.

Zhang et al. (2025) studied this question.

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View Full Paper

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Authors

Comprehensive review highlights machine learning's role in Parkinson's disease diagnosis, suggesting improvements in clinical practice.

Machine learning shows potential in enhancing the diagnosis of Parkinson's disease through various data modalities.
Among the 117 articles reviewed, only 28 demonstrated a low risk of bias, indicating a need for improved study designs.
The analysis covered diverse data types—such as voice and neuroimaging—with voice data being the most prevalent at 40.2%.
Current limitations of applying machine learning in clinical settings were discussed, highlighting areas for future research.

Zhang et al. (2025) studied this question.

View Full Paper

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