Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature

Abstract

Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.

Artificial Intelligence in Translational Medicine

“…Similarly, comparable procedures, extracting specific features from available data, allowing the development of ML-based models for Parkinson's disease (PD). In fact, as reported for AD, several studies highlighted that through ML-based approaches applied to PD [279], it is possible to predict the progression of the disorder by employing serum cytokines [280], MRI [281], and walking tests [282]; to estimate the state of PD, employing longitudinal data [283]; to rate the main symptoms (resting tremor and bradykinesia) [284]; and to produce a correct diagnosis from EEG analysis [285,286] and from voice datasets [287,288].…”

Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?

“…Moreover, criteria meeting Alzheimer’s disease pathology is present in nearly 80% of those with autopsy-confirmed PD [ 16 ], thus blurring the diagnostic distinction between PD and Alzheimer’s disease. While many AI models have been developed to measure PD based on motor symptoms and other phenotypic evidence [ 17 ], they cannot allay these fundamental limitations of diagnosis. Until AI is sufficiently developed as to elucidate the range of molecular pathways in PD, it can only assist in the movement disorder specialist’s clinical judgement.…”

Artificial Intelligence in Translational Medicine

“…Similarly, similar procedures, extracting specific features from available data allowing the development of ML based models for Parkinson's disease (PD). In fact, as reported for AD, several studies highlighted that through ML-based approaches applied to PD [268] is possible to predict the progression of the disorder employing serum cytokines [269], MRI [270], and walking tests [271], to estimate the state of PD, employing longitudinal data [272], to rate the main synthomps (resting tremor and bradykinesia) [273], to produce a correct diagnosis from EEG analysis [274,275] and from voice dataset [276,277], only for reporting some relevant works.…”