Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions

• Published in: Frontiers in aging neuroscience Vol. 15; p. 1216163
• Main Authors: Garcia Santa Cruz, Beatriz, Husch, Andreas, Hertel, Frank
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 19.07.2023; Frontiers Media S.A
• Subjects: Aging Neuroscience; Alzheimer's disease; Artificial intelligence; Biomarkers; Brain research; Clinical medicine; Cognitive ability; computer-aided diagnosis; Constipation; Deep learning; Diagnosis; Dopamine; Learning algorithms; Machine learning; Magnetic resonance imaging; Medical imaging; Medical treatment; Movement disorders; Nervous system; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; Older people; Olfaction disorders; Parkinson's disease; Patients; Physical therapy; Prognosis; Structure-function relationships; translational ML; Tremor (Muscular contraction); deep learning; Parkinson's disease; digital health; computer-aided diagnosis; machine learning; translational ML; neuroimaging
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2023.1216163

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.