Imagine your brain is a vast and mysterious jungle, full of hidden treasures waiting to be discovered. Well, that’s kind of like how researchers are using machine learning models to unlock the secrets of Parkinson’s disease in brain imaging! Parkinson’s is a complex and progressive disorder that affects both movement and thinking. With no cure available, early diagnosis and accurate prognosis are crucial for effective treatment. That’s where medical imaging, specifically MRI, comes into play. By using artificial intelligence techniques like deep learning and machine learning, scientists can analyze vast amounts of brain MRI data to find new patterns and relationships that were previously unknown. However, integrating these solutions into clinical practice comes with its own set of challenges. This review provides a comprehensive overview of recent machine learning techniques used for diagnosing and predicting the progression of Parkinson’s in brain MRI images. It tackles the challenges at different levels: the disease itself, the specific tasks involved, and the technology needed to make it all work. So, if you’re eager to dive into the exciting world of AI and brain imaging, check out the full article to learn about the future directions and potential breakthroughs on the horizon!

Parkinson’s disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease’s structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.

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