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Clin Neurol Neurosurg. 2019 Sep;184:105442. doi: 10.1016/j.clineuro.2019.105442. Epub 2019 Jul 16.

Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review.

Author information

1
Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia. Electronic address: minja.n.belic@gmail.com.
2
Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; School of Electrical Engineering, University of Belgrade, Belgrade, Serbia. Electronic address: vladislava.bobic@ic.etf.bg.ac.rs.
3
Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; School of Electrical Engineering, University of Belgrade, Belgrade, Serbia. Electronic address: milica.badza@ic.etf.bg.ac.rs.
4
School of Electrical Engineering, University of Belgrade, Belgrade, Serbia. Electronic address: sn183153m@student.etf.bg.ac.rs.
5
Innovation Center, School of Electrical Engineering, University of Belgrade, Belgrade, Serbia. Electronic address: milica.djuric@etf.bg.ac.rs.
6
Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia. Electronic address: vladimir.s.kostic@gmail.com.

Abstract

Artificial intelligence, specifically machine learning, has found numerous applications in computer-aided diagnostics, monitoring and management of neurodegenerative movement disorders of parkinsonian type. These tasks are not trivial due to high inter-subject variability and similarity of clinical presentations of different neurodegenerative disorders in the early stages. This paper aims to give a comprehensive, high-level overview of applications of artificial intelligence through machine learning algorithms in kinematic analysis of movement disorders, specifically Parkinson's disease (PD). We surveyed papers published between January 2007 and January 2019, within online databases, including PubMed and Science Direct, with a focus on the most recently published studies. The search encompassed papers dealing with the implementation of machine learning algorithms for diagnosis and assessment of PD using data describing motion of upper and lower extremities. This systematic review presents an overview of 48 relevant studies published in the abovementioned period, which investigate the use of artificial intelligence for diagnostics, therapy assessment and progress prediction in PD based on body kinematics. Different machine learning algorithms showed promising results, particularly for early PD diagnostics. The investigated publications demonstrated the potentials of collecting data from affordable and globally available devices. However, to fully exploit artificial intelligence technologies in the future, more widespread collaboration is advised among medical institutions, clinicians and researchers, to facilitate aligning of data collection protocols, sharing and merging of data sets.

KEYWORDS:

Artificial intelligence; Assessment; Diagnostics; Kinematics; Machine learning; Motion analysis; Parkinson’s disease

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