What the trained eye cannot see: Quantitative kinematics and machine learning detect movement deficits in early-stage Parkinson's disease from videos.

BACKGROUND: Evaluation of disease severity in Parkinson's disease (PD) relies on motor symptoms quantification. However, during early-stage PD, these symptoms are subtle and difficult to quantify by experts, which might result in delayed diagnosis and suboptimal disease management. OBJECTIVE: To evaluate the use of videos and machine learning (ML) for automatic quantification of motor symptoms in early-stage PD. METHODS: We analyzed videos of three movement tasks-Finger Tapping, Hand Movement, and Leg Agility- from 26 aged-matched healthy controls and 31 early-stage PD patients. Utilizing ML algorithms for pose estimation we extracted kinematic features from these videos and trained three classification models based on left and right-side movements, and right/left symmetry. The models were trained to differentiate healthy controls from early-stage PD from videos. RESULTS: Combining left side, right side, and symmetry features resulted in a PD detection accuracy of 79 % from Finger Tap videos, 75 % from Hand Movement videos, 79 % from Leg Agility videos, and 86 % when combining the three tasks using a soft voting approach. In contrast, the classification accuracy varied between 40 % and 72 % when the movement side or symmetry were not considered. CONCLUSIONS: Our methodology effectively differentiated between early-stage PD and healthy controls using videos of standardized motor tasks by integrating kinematic analyses of left-side, right-side, and bilateral symmetry movements. These results demonstrate that ML can detect movement deficits in early-stage PD from videos. This technology is easy-to-use, highly scalable, and has the potential to improve the management and quantification of motor symptoms in early-stage PD.

Characterizing Disease Progression in Parkinson's Disease from Videos of the Finger Tapping Test.

INTRODUCTION: Parkinson's disease (PD) is characterized by motor symptoms whose progression is typically assessed using clinical scales, namely the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Despite its reliability, the scale is bounded by a 5-point scale that limits its ability to track subtle changes in disease progression and is prone to subjective interpretations. We aimed to develop an automated system to objectively quantify motor symptoms in PD using Machine Learning (ML) algorithms to analyze videos and capture nuanced features of disease progression. METHODS: We analyzed videos of the Finger Tapping test, a component of the MDS-UPDRS, from 24 healthy controls and 66 PD patients using ML algorithms for hand pose estimation. We computed multiple movement features related to bradykinesia from videos and employed a novel tiered classification approach to predict disease severity that employed different features according to severity. We compared our video-based disease severity prediction approach against other approaches recently introduced in the literature. RESULTS: Traditional kinematics features such as amplitude and velocity changed linearly with disease severity, while other non-traditional features displayed non-linear trends. The proposed disease severity prediction approach demonstrated superior accuracy in detecting PD and distinguishing between different levels of disease severity when compared to existing approaches.

Skin Biopsy Detection of Phosphorylated α-Synuclein in Patients With Synucleinopathies.

Importance: Finding a reliable diagnostic biomarker for the disorders collectively known as synucleinopathies (Parkinson disease [PD], dementia with Lewy bodies [DLB], multiple system atrophy [MSA], and pure autonomic failure [PAF]) is an urgent unmet need. Immunohistochemical detection of cutaneous phosphorylated α-synuclein may be a sensitive and specific clinical test for the diagnosis of synucleinopathies. Objective: To evaluate the positivity rate of cutaneous α-synuclein deposition in patients with PD, DLB, MSA, and PAF. Design, Setting, and Participants: This blinded, 30-site, cross-sectional study of academic and community-based neurology practices conducted from February 2021 through March 2023 included patients aged 40 to 99 years with a clinical diagnosis of PD, DLB, MSA, or PAF based on clinical consensus criteria and confirmed by an expert review panel and control participants aged 40 to 99 years with no history of examination findings or symptoms suggestive of a synucleinopathy or neurodegenerative disease. All participants completed detailed neurologic examinations and disease-specific questionnaires and underwent skin biopsy for detection of phosphorylated α-synuclein. An expert review panel blinded to pathologic data determined the final participant diagnosis. Exposure: Skin biopsy for detection of phosphorylated α-synuclein. Main Outcomes: Rates of detection of cutaneous α-synuclein in patients with PD, MSA, DLB, and PAF and controls without synucleinopathy. Results: Of 428 enrolled participants, 343 were included in the primary analysis (mean [SD] age, 69.5 [9.1] years; 175 [51.0%] male); 223 met the consensus criteria for a synucleinopathy and 120 met criteria as controls after expert panel review. The proportions of individuals with cutaneous phosphorylated α-synuclein detected by skin biopsy were 92.7% (89 of 96) with PD, 98.2% (54 of 55) with MSA, 96.0% (48 of 50) with DLB, and 100% (22 of 22) with PAF; 3.3% (4 of 120) of controls had cutaneous phosphorylated α-synuclein detected. Conclusions and Relevance: In this cross-sectional study, a high proportion of individuals meeting clinical consensus criteria for PD, DLB, MSA, and PAF had phosphorylated α-synuclein detected by skin biopsy. Further research is needed in unselected clinical populations to externally validate the findings and fully characterize the potential role of skin biopsy detection of phosphorylated α-synuclein in clinical care.

The Vocal Flutter of Multiple System Atrophy: A Parkinsonian-Type Phenomenon?

BACKGROUND: Early features of multiple system atrophy (MSA) are similar to those in Parkinson's disease (PD), which can challenge differential diagnosis. Identifying clinical markers that help distinguish MSA from forms of parkinsonism is essential to promptly implement the most appropriate management plan. In the context of a thorough neurological evaluation, the presence of a vocal flutter might be considered a potential feature of MSA-parkinsonian type (MSA-P). CASES: This case series describes clinical histories of 3 individuals with MSA-P. In each case, vocal flutter was detected during neurological and motor speech evaluations. It seemed to be a concomitant feature with the constellation of other signs and symptoms that led to the clinical diagnosis. LITERATURE REVIEW: The vocal flutter may be described as pitch and loudness fluctuations during phonation. Different from a vocal tremor, the flutter phenomenon has higher oscillation frequencies. The neuropathological underpinnings of vocal flutter may be related to generalized laryngeal dysfunction that is commonly described in MSA-P. CONCLUSION: Vocal flutter may be a unique speech feature in some individuals who have MSA-P. Future studies using perceptual and acoustic measures of speech are warranted to quantify these observations and directly compare to other MSA variants, PD, and a control group.