Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos

Kontschieder, Peter; Dorn, Jonas F.; Morrison, Cecily; Corish, Robert; Zikic, Darko; Sellen, Abigail; D’Souza, Marcus; Kamm, Christian P.; Burggraaff, Jessica; Tewarie, Prejaas; Vogel, Thomas; Azzarito, Michela; Glocker, Ben; Chin, Peter; Dahlke, Frank; Polman, Chris H.; Kappos, Ludwig; Uitdehaag, Bernard M. J.; Criminisi, Antonio

doi:10.1007/978-3-319-10470-6_54

Cited by 28 publications

(19 citation statements)

References 13 publications

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“…We reviewed 8 validation studies using Kinect to measure upper extremity movement in disease indications including Adhesive Capsulitis [6], Stroke [7] and Multiple Sclerosis [13].…”

Section: Upper Extremity Movementmentioning

confidence: 99%

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A Review Evaluating the Validity of Motion-Based Gaming Platforms to Measure Clinical Outcomes in Clinical Research

Byrom

Breedon

Muehlhausen³

2016

Value in Health

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Results OBJECTIVES:Motion-based video game platforms provide the capability to track 3D body movements and may offer a versatile, easy to use and low-cost approach to measuring objective clinical outcomes. We reviewed published validation studies comparing clinical outcomes derived from video game platforms to gold-standard approaches. METHODS:We categorized studies in our review into three areas of application and summarized validation findings. We confined our review to studies using the Microsoft Kinect platform due to the volume of work in this area. RESULTS:Gait and balance: Five validation studies reported varied findings. One study in MS reported good correlation of most parameters with ClinROs; and a second study reported good validation of walk test parameters in Stroke patients. A treadmill test in healthy volunteers found Kinect underestimated joint flexion and over-estimated extension; and a further study was able to detect gait disturbances in MS during a speed-walking test compared to healthy volunteers although correlation to clinician assessment was modest (r=0.447). Kinect use during a battery of balance and dexterity tests in PD accurately measured the timing (ICCs: 0.940-0.999) and gross spatial characteristics of clinically relevant movements, but spatial accuracy for smaller movements, such as toe tapping (ICC = 0.038), was poor. Upper extremity movement: Eight studies reported good validity in measurement of shoulder range of motion (r's > 0.8, ICCs > 0.864). Spirometry: One study reported strong correlation of spirometry parameters (r>0.866) estimated using multiple sensors to generate a 3D image of the chest. CONCLUSIONS:Motion-based video gaming platforms offer potential for low-cost assessment of movement and mobility in large-scale clinical trials without reliance on specialist centers. Studies report good validity in some application areas. The ability to provide the level of accuracy needed in more rapid and finer movements requires more validation work.

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“…We reviewed 8 validation studies using Kinect to measure upper extremity movement in disease indications including Adhesive Capsulitis [6], Stroke [7] and Multiple Sclerosis [13].…”

Section: Upper Extremity Movementmentioning

confidence: 99%

“…One study [11], used a performance task (moving a book from one location to another while seated at a desk) to measure smoothness of motion and identify involuntary movements and dyskinesia. A further study [13] used a machine learning approach to use the 3D movement data to successfully distinguish MS patients from healthy controls.…”

Section: Spiromterymentioning

confidence: 99%

A Review Evaluating the Validity of Motion-Based Gaming Platforms to Measure Clinical Outcomes in Clinical Research

Byrom

Breedon

Muehlhausen³

2016

Value in Health

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“…While they can be specifically designed for an application if some domain knowledge is available, a popular and effective approach [3][4][5][6][7][8][9] consists in extracting a large number of low-level Haar-like features corresponding to visual cues at offset locations. Each Haar-like feature is characterized by a parameter vector λ ∈ Λ which defines, for each pixel p, a certain type of contextual information x λ (p) ∈ R as follows.…”

Section: Haar-like Features For Segmentationmentioning

confidence: 99%

“…Recent applications of the forest framework to the medical field include multi-organ segmentation within computed tomography (CT) volumes [3], segmentation of the midbrain in transcranial ultrasound volumes [4], multi-organ localization in magnetic resonance (MR) [5] and CT [6] data, semantic labeling of brain structures in MR scans [7], depth video classification to quantify the progression of multiple sclerosis [8], and localization of anatomical landmarks within hand MR scans [9].…”

Section: Introductionmentioning

confidence: 99%

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Scale-Adaptive Forest Training via an Efficient Feature Sampling Scheme

Peter

Pauly

Chatelain

et al. 2015

Lecture Notes in Computer Science

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Abstract. In the context of forest-based segmentation of medical data, modeling the visual appearance around a voxel requires the choice of the scale at which contextual information is extracted, which is of crucial importance for the final segmentation performance. Building on Haar-like visual features, we introduce a simple yet effective modification of the forest training which automatically infers the most informative scale at each stage of the procedure. Instead of the standard uniform sampling during node split optimization, our approach draws candidate features sequentially in a fine-to-coarse fashion. While being very easy to implement, this alternative is free of additional parameters, has the same computational cost as a standard training and shows consistent improvements on three medical segmentation datasets with very different properties.

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Ranking of Dystonia Severity by Pairwise Video Comparison

Ellermeyer

Otte

Heinrich

et al. 2016

Movement Disord Clin Pract

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Background: Reviewers of dystonia rating scales agree on the need to assess symptoms more comprehensively. During the development of a quantitative dystonia assessment by video-perceptive computing, we devised a video-based severity ranking as a procedure to create a validation standard without the use of clinical scales. Methods: Thirty-four patients with dystonia (17 with dystonic tremor) and 2 controls were assessed with clinical scales and video-recordings of 24 short movement tasks. Two to 4 raters compared multiple permutations of videos from 22 subjects, including 2 healthy controls, until a complete rank order was achieved. Inter-rater agreement was expressed as normalized Kendall tau distance. Spearman correlations of video rank order with clinical scales and self-rating were repeated for tremor/nontremor subgroups. Results: Normalized Kendall tau distances were <0.3 for 15 items. The video rank order for sitting and head movements correlated with clinical scales for the whole group (rho 0.52-0.87) and in the subgroup without tremor. In the tremor subgroup such correlation was perceived in the 2 items involving sitting. Video rank order correlated with quality of life self-rating only in 1 item (arms held in front, palm down). Conclusions: The agreement of video rankings between raters is remarkable. The lack of correlation in the tremor subgroup in several items may be interpreted as tremor being considered in video comparisons but not in clinical scales. This supports video-based ranking as a more comprehensive rating of dystonia and as a possible validation instrument applicable in situations in which no reference standard is available Dystonia is a movement disorder characterized by motor symptoms such as sustained or intermittent involuntary muscle contractions.

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Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos

Cited by 28 publications

References 13 publications

A Review Evaluating the Validity of Motion-Based Gaming Platforms to Measure Clinical Outcomes in Clinical Research

A Review Evaluating the Validity of Motion-Based Gaming Platforms to Measure Clinical Outcomes in Clinical Research

Scale-Adaptive Forest Training via an Efficient Feature Sampling Scheme

Ranking of Dystonia Severity by Pairwise Video Comparison

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