Quantifying Parkinson’s disease motor severity under uncertainty using MDS-UPDRS videos

Lu, Mandy; Zhao, Qingyu; Poston, Kathleen L.; Sullivan, Edith V.; Pfefferbaum, Adolf; Shahid, Marian; Katz, Maya; Kouhsari, Leila Montaser; Milstein, Arnold; Niebles, Juan Carlos; Henderson, Victor W.; Li, Feifei; Pohl, Kilian M.; Adeli, Ehsan

doi:10.1016/j.media.2021.102179

Cited by 58 publications

(53 citation statements)

References 40 publications

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“…In addition, the computation of the MC method [1] takes a long time due to its high complexity. (2) In about 75% of the cases, the shared backbone (F 1 = F 2 ) for feature extraction gives higher accuracies than the independent backbone (F 1 = F 2 ). (3) When using the CNN for the relation regression, models using SFCN as the backbone provides lower MAEs than models using mSFCN as the backbone.…”

Section: B Accuracy Of Relation-based Brain Age Estimationmentioning

confidence: 99%

“…Regression aims to estimate continuous values (or ordinal outcomes [1]) from the input data using machine learning models. It has many applications such as severity scores [2], [3], brain age estimation [4], [5] and fluid intelligence prediction [6]. Deep convolutional neural networks (CNNs) can transform the raw input image data into target variables by training on a large-scale dataset [7].…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of the proposed deep relation learning are summarized as follows: (1) it is an extension of the order learning. When only "relative relation" r 2 = τ x −τ y is applied, it becomes the deep order learning [1]; (2) the values of the x and y can be directly estimated from the relations r i , i ∈ {1, 2, 3, 4}; (3) mathematically, a relation is reflexivity if each element is related to itself. The proposed four relations are reflexivity [12] because the model can compute the relations between the pair of the same image (x, x) and r 1 = 2τ x , r 2 = 0, r 3 = r 4 = τ x .…”

Section: Introductionmentioning

confidence: 99%

“…shows that the MAE (2.38 years) of using the four different relations is lower than the MAE (2.55 years) of the order learning based on the MC rules[1] (2). We evaluated different strategies to estimate the brain ages of a pair of input images: brain age estimation with different test images (strategies S 1−3 in TableV), brain age estimation with the reference with known age (strategies S 4−9 in TableV) and brain age estimation with a pair of the same image (strategies S 10−16 in TableV).…”

mentioning

confidence: 99%

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Deep Relation Learning for Regression and Its Application to Brain Age Estimation

He¹,

Feng²,

Grant³

et al. 2022

Preprint

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Most deep learning models for temporal regression directly output the estimation based on single input images, ignoring the relationships between different images. In this paper, we propose deep relation learning for regression, aiming to learn different relations between a pair of input images. Four nonlinear relations are considered: "cumulative relation", "relative relation", "maximal relation" and "minimal relation". These four relations are learned simultaneously from one deep neural network which has two parts: feature extraction and relation regression. We use an efficient convolutional neural network to extract deep features from the pair of input images and apply a Transformer for relation learning. The proposed method is evaluated on a merged dataset with 6,049 subjects with ages of 0-97 years using 5-fold cross-validation for the task of brain age estimation. The experimental results have shown that the proposed method achieved a mean absolute error (MAE) of 2.38 years, which is lower than the MAEs of 8 other state-of-the-art algorithms with statistical significance (p<0.05) in paired T-test (two-side).

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Section: B Accuracy Of Relation-based Brain Age Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Deep Relation Learning for Regression and Its Application to Brain Age Estimation

He¹,

Feng²,

Grant³

et al. 2022

Preprint

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“…This score is worldwide-used for patient follow-up in outpatient clinic but also in clinical research and more specifically in therapeutic trials. However, the semi-quantitative assessment of parkinsonian symptoms by the MDS-UPDRS III score suffers from a certain subjectivity and the reproducibility of the measurements arising from assessors is questionable especially in case of nonparkinsonian expertise [7][8][9][10][11][12][13]. This may contribute to the difficulty in the follow-up of PD patients and also to induce some biases in clinical research due to the multiplicity of assessors and clinical centers, and variability across longitudinal iterative visits.…”

Section: Introductionmentioning

confidence: 99%

Video-based automated analysis of MDS-UPDRS III parameters in Parkinson disease

Vignoud

Desjardins

Salardaine

et al. 2022

Preprint

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BackgroundAmong motor symptoms of Parkinson’s disease (PD), including rigidity and resting tremor, bradykinesia is a mandatory feature to define the parkinsonian syndrome. MDS-UPDRS III is the worldwide reference scale to evaluate the parkinsonian motor impairment, especially bradykinesia. However, MDS-UPDRS III constitutes an agent-based score making reproducible measurements and follow-up challenging.ObjectivesUsing a deep learning approach, we developed a tool to compute an objective score of bradykinesia based on the gold-standard MDS-UPDRS III.MethodsIn the Movement Disorder unit of Avicenne University Hospital, we acquired a large database of videos of parkinsonian patients performing MDS-UPDRS III protocols. We applied two deep learning algorithms to detect a two-dimensional (2D) skeleton of the hand composed of 21 predefined points, and transposed it into a three-dimensional (3D) skeleton.ResultsWe developed a 2D and 3D automated analysis tool to study the evolution of several key parameters during the protocol repetitions of the MDS-UPDRS III. Scores from 2D automated analysis showed a significant correlation with gold-standard ratings of MDS-UPDRS III, measured with coefficients of determination for the tapping (0.609) and hand movements (0.701) protocols using decision tree algorithms. The individual correlations of the different parameters measured with MDS-UPDRS III scores carry meaningful information and are consistent with MDS-UPDRS III guidelines.ConclusionWe developed a deep learning-based tool to reliably score and analyze bradykinesia for parkinsonian patients.

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Learning Hand Kinematics for Parkinson's Disease Assessment Using a Multimodal Sensor Glove

Li,

Yin,

Liu

et al. 2023

Advanced Science

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Hand dysfunctions inParkinson's disease include rigidity, muscle weakness, and tremor, which can severely affect the patient's daily life. Herein, a multimodal sensor glove is developed for quantifying the severity of Parkinson's disease symptoms in patients' hands while assessing the hands' multifunctionality. Toward signal processing, various algorithms are used to quantify and analyze each signal: Exponentially Weighted Average algorithm and Kalman filter are used to filter out noise, normalization to process bending signals, K-Means Cluster Analysis to classify muscle strength grades, and Back Propagation Neural Network to identify and classify tremor signals with an accuracy of 95.83%. Given the compelling features, the flexibility, muscle strength, and stability assessed by the glove and the clinical observations are proved to be highly consistent with Kappa values of 0.833, 0.867, and 0.937, respectively. The intraclass correlation coefficients obtained by reliability evaluation experiments for the three assessments are greater than 0.9, indicating that the system is reliable. The glove can be applied to assist in formulating targeted rehabilitation treatments and improve hand recovery efficiency.

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Quantifying Parkinson’s disease motor severity under uncertainty using MDS-UPDRS videos

Cited by 58 publications

References 40 publications

Deep Relation Learning for Regression and Its Application to Brain Age Estimation

Deep Relation Learning for Regression and Its Application to Brain Age Estimation

Video-based automated analysis of MDS-UPDRS III parameters in Parkinson disease

Learning Hand Kinematics for Parkinson's Disease Assessment Using a Multimodal Sensor Glove

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