Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions
GARCIA SANTA CRUZ, Beatriz; HUSCH, Andreas; HERTEL, Frank
Parkinson's disease,neurodegeneration,Neuroimaging,machine learning,deep learning,computer-aided-diagnosis,Digital Health
Résumé :
[en] 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
Disciplines :
Sciences de la santé humaine: Multidisciplinaire, généralités & autres
HUSCH, Andreas ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Interventional Neuroscience
HERTEL, Frank ; Centre Hospitalier de Luxembourg > Service National du Neurochirurgie
Co-auteurs externes :
no
Titre :
Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions
FNR12244779 - Molecular, Organellar And Cellular Quality Control In Parkinson'S Disease And Other Neurodegenerative Diseases, 2017 (01/05/2018-31/10/2024) - Jens Schwamborn
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