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Book Sections Year : 2023

Machine learning for Parkinson’s disease and related disorders

Abstract

Parkinson’s disease is a complex heterogeneous neurodegenerative disorder characterized by the loss of dopamine neurons in the basal ganglia, resulting in many motor and non-motor symptoms. Although there is no cure to date, the dopamine replacement therapy can improve motor symptoms and the quality of life of the patients. The cardinal symptoms of this disorder are tremor, bradykinesia and rigidity, referred to as parkinsonism. Other related disorders, such as dementia with Lewy bodies, multiple system atrophy and progressive supranuclear palsy, share similar motor symptoms although they have different pathophysiology and are less responsive to the dopamine replacement therapy. Machine learning can be of great utility to better understand Parkinson’s disease and related disorders and to improve patient care. Many challenges are still open, including early accurate diagnosis, differential diagnosis, better understanding of the pathologies, symptom detection and quantification, individual disease progression prediction, and personalized therapies. In this chapter, we review research works on Parkinson’s disease and related disorders using machine learning.
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hal-03830073 , version 1 (26-10-2022)
hal-03830073 , version 2 (03-10-2023)

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Johann Faouzi, Olivier Colliot, Jean-Christophe Corvol. Machine learning for Parkinson’s disease and related disorders. Olivier Colliot. Machine Learning for Brain Disorders, Springer, 2023, ⟨10.1007/978-1-0716-3195-9_26⟩. ⟨hal-03830073v1⟩
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