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Ignazio Aleo

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2 records found

Towards automated video-based assessment of dystonia in dyskinetic cerebral palsy

A novel approach using markerless motion tracking and machine learning

Journal article - Helga Haberfehlner, Helga Haberfehlner, Helga Haberfehlner, Shankara S. van de Ven, Sven A. van der Burg, Florian Huber, Florian Huber, Sonja Georgievska, Ignazio Aleo, J. Harlaar, Laura A. Bonouvrié, Marjolein M. van der Krogt, Marjolein M. van der Krogt, Annemieke I. Buizer, Annemieke I. Buizer, Annemieke I. Buizer

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.@en

Home‐Based Measurements of Dystonia in Cerebral Palsy Using Smartphone‐Coupled Inertial Sensor Technology and Machine Learning

A Proof‐of‐Concept Study

Journal article - Dylan Den Hartog, Marjolein M. van der Krogt, Marjolein M. van der Krogt, Sven van der Burg, Ignazio Aleo, Johannes Gijsbers, Laura A. Bonouvrié, Laura A. Bonouvrié, J. Harlaar, Annemieke I. Buizer, Annemieke I. Buizer, Helga Haberfehlner, Helga Haberfehlner, Helga Haberfehlner

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 smartphonecoupled 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.@en

Contributed

1 records found

Quantitative comparison and harmonisation of three biomechanical models used in gait analysis

Master thesis - M.R. Piccoli Gajek, J. Harlaar, Ignazio Aleo

Introduction: Clinical gait analysis is used to assess patients' impairments, improve treatment decision-making and monitor patients’ progress. Motion capture of markers placed on the skin is the gold standard technique for gait analysis. Markers referenced to the underlying bones are used to define a biomechanical model, i.e. an anatomical frame for each body segment. Several biomechanical models have been proposed, and none can be considered as ground truth. The outcomes from different biomechanical models provide non comparable data, this hampers data sharing and impedes the full potential of clinical interpretation and treatment recommendation. Objectives: This study explores the differences in biomechanical models’ definitions used for gait analysis and evaluates four harmonisation techniques with the aim to improve kinematic data comparability. Methods: Nine healthy participants performed a walking task. A merged markerset was developed by including three widely adopted biomechanical models: CGM, CAST and LAMB. The harmonisation processes involved three coordinate transformations between anatomical frames based on the calibration trial, the mean of the task trial and the frame-by-frame of the task trial. The fourth harmonisation consisted in inverse kinematics fitting based on the same underlying model (OpenSim). Differences before and after each harmonisation approach were analysed by considering joint kinematics. Results: The differences between the native biomechanical models' definitions reach 23.2° of rotation and 41mm of translation. A systematic difference between models was found, which was higher than between subjects, and varied across the gait cycle with a nonlinear pattern. All harmonisation processes improved kinematic data comparability. No statistically significant difference between the curves was found when results were harmonised with frame-by-frame coordinate transformation. Harmonisation based on inverse kinematics provides comparable results, with the exception of ankle parameters on sagittal plane. Discussion: The nonlinear and gait cycle-dependent systematic difference between biomechanical models is suggested to be due to the soft tissue artefact. As soft tissue artefact varies across the gait cycle and between subjects, it is reasonable that a frame-by-frame harmonisation provides more comparable results, as the dynamic transformation takes into account the nonlinear soft tissue behavior. However, it remains specific for gait. Conclusion: This study provides promising methodologies for kinematic data harmonisation and allows to easily switch between biomechanical models without gaining consensus on which biomechanical model should be mostly used. However, further investigation of soft tissue artefact effects on biomechanical models’ definitions and consequent joint kinematics is still required to approximate the ground truth.