What type of study is this?

This is a Systematic Review study.

What question did this study set out to answer?

To systematically review machine learning and deep learning techniques for diagnosing and prognosing Parkinson’s disease.

February 8, 2026

Parkinson’s disease Diagnosis and prognosis: A Systematic Review of Machine Learning and Deep Learning with Emphasis on Model Optimization

Key Points

To systematically review machine learning and deep learning techniques for diagnosing and prognosing Parkinson’s disease.
Comprehensive review of existing AI methods for PD diagnosis and prognosis
Focus on optimization strategies and transfer learning approaches
Examine challenges in practical implementation and datasets available
Identified key strategies to enhance diagnostic accuracy using AI
Highlighted limitations and performance indicators of current approaches
Proposed pathways for integrating multimodal data and improving model interpretability

Abstract

Parkinson’s disease (PD) is a progressive and complex neurodegenerative disorder associated with ageing, affecting both motor and cognitive functions equally. Since there is currently no cure, early diagnosis and accurate prognosis are essential to enhance treatment efficacy and maintain symptom management. Medical imaging, especially magnetic resonance imaging (MRI), has emerged as a vital tool for developing support systems that assist in diagnosis and prognosis. Conventional clinical evaluations that rely on neurological examinations and symptom monitoring pose challenges in early and precise detection of issues. These assessments lack significant sensitivity and are primarily subjective in nature. Recent developments in artificial intelligence, particularly machine learning (ML) and deep learning (DL), demonstrate considerable potential for improving the diagnosis and monitoring of Parkinson’s disease (PD) by analyzing complex biomedical data such as voice recordings, gait parameters, handwriting patterns, and neuroimaging findings. This thorough review of contemporary machine learning and deep learning techniques concentrates on optimization strategies, transfer learning approaches, and fundamental methods for improving diagnostic accuracy. The study examines challenges in practical implementation and assesses the advantages, limitations, datasets, and performance indicators of these approaches. We identify key areas where research is insufficient and propose potential pathways forward, including the integration of multimodal data, the development of AI models that are interpretable by laypersons, and the application of federated learning to create more reliable and scalable Parkinson’s disease diagnostic systems for clinical implementation.

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