Attainment of personal goals in the first year of intrathecal baclofen treatment in dyskinetic cerebral palsy: a prospective cohort study

Pharmacological and neurosurgical management of cerebral palsy and dystonia: Clinical practice guideline update

Dystonia, typically characterized by slow repetitive involuntary movements, stiff abnormal postures, and hypertonia, is common among individuals with cerebral palsy (CP). Dystonia can interfere with activities and have considerable impact on motor function, pain/comfort, and ease of caregiving. Although pharmacological and neurosurgical approaches are used clinically in individuals with CP and dystonia that is causing interference, evidence to support these options is limited. This clinical practice guideline update comprises 10 evidence-based recommendations on the use of pharmacological and neurosurgical interventions for individuals with CP and dystonia causing interference, developed by an international expert panel following the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach. The recommendations are intended to help inform clinicians in their use of these management options for individuals with CP and dystonia, and to guide a shared decision-making process in selecting a management approach that is aligned with the individual's and the family's values and preferences.

Towards functional improvement of motor disorders associated with cerebral palsy

Cerebral palsy is a lifelong neurodevelopmental condition arising from non-progressive disorders occurring in the fetal or infant brain. Cerebral palsy has long been categorised into discrete motor types based on the predominance of spasticity, dyskinesia, or ataxia. However, these motor disorders, muscle weakness, hypotonia, and impaired selective movements should also be discriminated across the range of presentations and along the lifespan. Although cerebral palsy is permanent, function changes across the lifespan, indicating the importance of interventions to improve outcomes in motor disorders associated with the condition. Mounting evidence exists for the inclusion of several interventions, including active surveillance, adapted physical activity, and nutrition, to prevent secondary and tertiary complications. Avenues for future research include the development of evidence-based recommendations, low-cost and high-quality alternatives to existing therapies to ensure universal access, standardised cerebral palsy registers to harmonise epidemiological and clinical information, improved adult screening and check-up programmes to facilitate positive lived experiences, and phase 3 trials for new interventions.

Towards automated video-based assessment of dystonia in dyskinetic cerebral palsy: A novel approach using markerless motion tracking and machine learning

Introduction: Video-based clinical rating plays an important role in assessing dystonia and monitoring the effect of treatment in dyskinetic cerebral palsy (CP). However, evaluation by clinicians is time-consuming, and the quality of rating is dependent on experience. The aim of the current study is to provide a proof-of-concept for a machine learning approach to automatically assess scoring of dystonia using 2D stick figures extracted from videos. Model performance was compared to human performance. Methods: A total of 187 video sequences of 34 individuals with dyskinetic CP (8-23 years, all non-ambulatory) were filmed at rest during lying and supported sitting. Videos were scored by three raters according to the Dyskinesia Impairment Scale (DIS) for arm and leg dystonia (normalized scores ranging from 0-1). Coordinates in pixels of the left and right wrist, elbow, shoulder, hip, knee and ankle were extracted using DeepLabCut, an open source toolbox that builds on a pose estimation algorithm. Within a subset, tracking accuracy was assessed for a pretrained human model and for models trained with an increasing number of manually labeled frames. The mean absolute error (MAE) between DeepLabCut's prediction of the position of body points and manual labels was calculated. Subsequently, movement and position features were calculated from extracted body point coordinates. These features were fed into a Random Forest Regressor to train a model to predict the clinical scores. The model performance trained with data from one rater evaluated by MAEs (model-rater) was compared to inter-rater accuracy. Results: A tracking accuracy of 4.5 pixels (approximately 1.5 cm) could be achieved by adding 15-20 manually labeled frames per video. The MAEs for the trained models ranged from 0.21 ± 0.15 for arm dystonia to 0.14 ± 0.10 for leg dystonia (normalized DIS scores). The inter-rater MAEs were 0.21 ± 0.22 and 0.16 ± 0.20, respectively. Conclusion: This proof-of-concept study shows the potential of using stick figures extracted from common videos in a machine learning approach to automatically assess dystonia. Sufficient tracking accuracy can be reached by manually adding labels within 15-20 frames per video. With a relatively small data set, it is possible to train a model that can automatically assess dystonia with a performance comparable to human scoring.

Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study

Accurate and reliable measurement of the severity of dystonia is essential for the indication, evaluation, monitoring and fine-tuning of treatments. Assessment of dystonia in children and adolescents with dyskinetic cerebral palsy (CP) is now commonly performed by visual evaluation either directly in the doctor’s office or from video recordings using standardized scales. Both methods lack objectivity and require much time and effort of clinical experts. Only a snapshot of the severity of dyskinetic movements (i.e., choreoathetosis and dystonia) is captured, and they are known to fluctuate over time and can increase with fatigue, pain, stress or emotions, which likely happens in a clinical environment. The goal of this study was to investigate whether it is feasible to use home-based measurements to assess and evaluate the severity of dystonia using smartphone-coupled inertial sensors and machine learning. Video and sensor data during both active and rest situations from 12 patients were collected outside a clinical setting. Three clinicians analyzed the videos and clinically scored the dystonia of the extremities on a 0–4 scale, following the definition of amplitude of the Dyskinesia Impairment Scale. The clinical scores and the sensor data were coupled to train different machine learning models using cross-validation. The average F1 scores (0.67 ± 0.19 for lower extremities and 0.68 ± 0.14 for upper extremities) in independent test datasets indicate that it is possible to detected dystonia automatically using individually trained models. The predictions could complement standard dyskinetic CP measures by providing frequent, objective, real-world assessments that could enhance clinical care. A generalized model, trained with data from other subjects, shows lower F1 scores (0.45 for lower extremities and 0.34 for upper extremities), likely due to a lack of training data and dissimilarities between subjects. However, the generalized model is reasonably able to distinguish between high and lower scores. Future research should focus on gathering more high-quality data and study how the models perform over the whole day.