A Video-Based Method for Objectively Rating Ataxia (original) (raw)
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A Video-Based Method for Automatically Rating Ataxia
2017
For many movement disorders, such as Parkinson’s disease and ataxia, disease progression is visually assessed by a clinician using a numerical disease rating scale. These tests are subjective, time-consuming, and must be administered by a professional. This can be problematic where specialists are not available, or when a patient is not consistently evaluated by the same clinician. We present an automated method for quantifying the severity of motion impairment in patients with ataxia, using only video recordings. We consider videos of the finger-to-nose test, a common movement task used as part of the assessment of ataxia progression during the course of routine clinical checkups. Our method uses neural network-based pose estimation and optical flow techniques to track the motion of the patient’s hand in a video recording. We extract features that describe qualities of the motion such as speed and variation in performance. Using labels provided by an expert clinician, we train a su...
medRxiv (Cold Spring Harbor Laboratory), 2024
Gait disturbances are the clinical hallmark of ataxia disorders, fundamentally impairing the mobility of ataxia patients. In clinical routine and research the severity of the gait disturbances is assessed within a well-established clinical scale and graded into categorial levels. Sensor-free motion registration and subsequent movement analysis allowed to overcome the obvious shortcoming of such coarse grading: Using time series models (tsfresh, ROCKET) we were not only able to successfully reproduce the categorial scaling (Human performance: 44.88% F1-score; our model: 80.28% F1-score). Particularly subtle, early gait disturbances and longitudinal progression below the perception threshold of the human examiner could be captured (Pearson's correlation coefficient human performance-0.060, not significant; our model:-0.626, p < 0.01). Furthermore, SHAP analysis allowed to identify the most important features for each clinical level of gait deterioration. This could further improve the sensitivity to capture longitudinal changes tailored to the pre-existing level of gait disturbances (Pearson's correlation coefficients up to-0.988, p < 0.01). In conclusion, the ML-based analysis could significantly improve the sensitivity in the assessment of gait disturbances in ataxia patients. Thus, it qualifies as a potential digital outcome parameter for early interventions, therapy monitoring, and home recordings.
Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait Task Videos
arXiv (Cornell University), 2022
In this paper, we investigated whether we can 1) detect participants with ataxia-specific gait characteristics (risk-prediction), and 2) assess severity of ataxia from gait (severityassessment). We collected 155 videos from 89 participants, 24 controls and 65 diagnosed with (or are pre-manifest) spinocerebellar ataxias (SCAs), performing the gait task of the Scale for the Assessment and Rating of Ataxia (SARA) from 11 medical sites located in 8 different states in the United States. We developed a method to separate the participants from their surroundings and constructed several features to capture gait characteristics like step width, step length, swing, stability, speed, etc. Our risk-prediction model achieves 83.06% accuracy and an 80.23% F1 score. Similarly, our severity-assessment model achieves a mean absolute error (MAE) score of 0.6225 and a Pearson's correlation coefficient score of 0.7268. Our models still performed competitively when evaluated on data from sites not used during training. Furthermore, through feature importance analysis, we found that our models associate wider steps, decreased walking speed, and increased instability with greater ataxia severity, which is consistent with previously established clinical knowledge. Our models create possibilities for remote ataxia assessment in non-clinical settings in the future, which could significantly improve accessibility of ataxia care. Furthermore, our * Equal contribution † Equal contribution
An automated form of video image analysis applied to classification of movement disorders
Disability and Rehabilitation
Video image analysis is able to provide quantitative data on postural and movement abnormalities and thus has an important application in neurological diagnosis and management. The conventional techniques require patients to be videotaped while wearing markers in a highly structured laboratory environment. This restricts the utility of video in routine clinical practise. We have begun development of intelligent software which aims to provide a more¯exible system able to quantify human posture and movement directly from whole-body images without markers and in an unstructured environment. The steps involved are to extract complete human pro® les from video frames, to ® t skeletal frameworks to the pro® les and derive joint angles and swing distances. By this means a given posture is reduced to a set of basic parameters that can provide input to a neural network classi® er. To test the system's performance we videotaped patients with dopa-responsive Parkinsonism and age-matched normals during several gait cycles, to yield 61 patient and 49 normal postures. These postures were reduced to their basic parameters and fed to the neural network classi® er in various combinations. The optimal parameter sets (consisting of both swing distances and joint angles) yielded successful classi®cation of normals and patients with an accuracy above 90 %. This result demonstrated the feasibility of the approach. The technique has the potential to guide clinicians on the relative sensitivity of speci® c postural} gait features in diagnosis. Future studies will aim to improve the robustness of the system in providing accurate parameter estimates from subjects wearing a range of clothing, and to further improve discrimination by incorporating more stages of the gait cycle into the analysis.
Nature Medicine
Friedreichʼs ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of person...
IEEE access, 2024
P ARKINSON'S disease (PD) is a neurological disorder caused by degeneration of dopaminergic neurons in the midbrain. PD patients mainly suffer from motor symptoms, which significantly impact their daily lives. The diagnostic criteria for PD include the presence of muscle rigidity, tremor, and postural reflex disturbances. The Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the standard tool for evaluating PD symptoms, part III of which is dedicated to motor symptoms. That part involves a comprehensive set of specific physical examinations, and physicians assign semi quantitative scores from 0 to 4. However, this approach faces notable challenges, including the requirement for movement-disorder experts proficient in using MDS-UPDRS and the presence of substantial inter rater variability even among experts. Overcoming these challenges requires a quantitative and objective assessment method. Given that the rating of motor symptoms predominantly involves assessing kinematic characteristics, the integration of sensor-based devices and machine learning techniques holds the potential to outperform human experts in symptom evaluations. This study used the Leap Motion optical motioncapture device to quantitatively measure and analyze hand movements while 45 PD patients performed the following 3 tasks from the MDS-UPDRS part III: finger tapping (FT), hand opening and closing (OC), and forearm pronation and supination (PS). Data from these tasks were collected and processed, resulting in the extraction of 31 movement patterns for each task. Additionally, 69 statistical features were extracted from each movement pattern, yielding 2139 features for each task. We subsequently employed a random forest algorithm to select the top 15% of features based on the reduction of Gini impurity. These selected features were subsequently fed into a sequential-forward-floating-selection algorithm, combined with a support vector machine, to identify relevant feature combinations and predict the severity of the motor symptoms. The classification accuracy was 87.0% for FT, 93.2% for OC, and 92.2% for PS. One-way analysis of variance identified 13 features of the OC task that were significantly more discriminative for classifying the movement disability of PD patients (p<0.05). This study highlights the effectiveness of combining sensorbased measurements with machine learning for symptom assessment, which demonstrated performance comparable to that of expert physicians. Implementing these findings in clinical settings holds the promise of applying objective and quantitative methods for evaluating the symptoms of PD and related disorders.
Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease
Sensors
The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson’s Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson’s Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extr...
Automated Assessment of Motor Impairments in Parkinson’s Disease
2020
A system for the automatic assessment of motor impairments in Parkinson’s Disease (PD) is presented. The interface, built around optical RGB-Depth devices, allows for tracking of hands and body movements during the performance of standard upper and lower limb tasks, as specified by the Unified Parkinson’s Disease Rating Scale (UPDRS). The assessment of the different tasks is performed by machine learning techniques. Selected kinematic parameters characterizing the movements are input to trained classifiers to rate the motor performance. The accurate tracking and characterization of the movements allows for an automatic and objective assessment of the UPDRS tasks, making feasible the monitoring of motor fluctuations also at-home for telemedicine or neurorehabilitation purposes.
Using AI to measure Parkinson’s disease severity at home
npj Digital Medicine, 2023
We present an artificial intelligence (AI) system to remotely assess the motor performance of individuals with Parkinson's disease (PD). In our study, 250 global participants performed a standardized motor task involving finger-tapping in front of a webcam. To establish the severity of Parkinsonian symptoms based on the finger-tapping task, three expert neurologists independently rated the recorded videos on a scale of 0-4, following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The inter-rater reliability was excellent, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists' ratings. Our machine learning model trained on these measures outperformed two MDS-UPDRS certified raters, with a mean absolute error (MAE) of 0.58 points compared to the raters' average MAE of 0.83 points. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care.
Automated machine vision enabled detection of movement disorders from hand drawn spirals
2020 IEEE International Conference on Healthcare Informatics (ICHI), 2020
A widely used test for the diagnosis of Parkinson’s disease (PD) and Essential tremor (ET) is hand-drawn shapes, where the analysis is observationally performed by the examining neurologist. This method is subjective and is prone to bias amongst different physicians. Due to the similarities in the symptoms of the two diseases, they are often misdiagnosed. Studies which attempt to automate the process typically use digitized input, where the tablet or specialized equipment are not affordable in many clinical settings. This study uses a dataset of scanned pen and paper drawings and a convolutional neural network (CNN) to perform classification between PD, ET and control subjects. The discrimination accuracy of PD from controls was 98.2%. The discrimination accuracy of PD from ET and from controls was 92%. An ablation study was conducted and indicated that correct hyper-parameter optimization can increases the accuracy up to 4.33%. Finally, the study indicates the viability of using a ...