Computer-based video analysis identifies infants with absence of fidgety movements

Støen, Ragnhild; Songstad, Nils Thomas; Silberg, Inger Elisabeth; Fjørtoft, Toril; Jensenius, Alexander Refsum; Adde, Lars

doi:10.1038/pr.2017.121

Cited by 37 publications

(65 citation statements)

References 29 publications

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“…The proportion (%) of time periods with abnormal movements identified by the CIMA model was shown to outperform the CP prediction ability of the standard deviation of the center of motion (C SD ) used in several previous computer-based studies by our group [12,30]. The previously developed C SD was based on a frame differencing method which may be susceptible to differences in contrasts, light, and infant clothing, which may vary more in this larger multi-site cohort of infants.…”

Section: Discussionmentioning

confidence: 91%

“…Furthermore, as the sample size and heterogeneity of children with CP increase, it becomes more challenging for a single predefined feature, such as C SD , to contain information of various characteristics of the infant movement repertoire relevant for a clinical outcome such as CP. Thus, we argue that it is likely that the predictive performance of other suggested single features such as relative movement frequency [15] and mean and minimum velocity [30] will potentially decay in larger multi-site populations of high-risk infants. The performance of the presented CIMA model suggests that overall variables, such as the proportion (%) of periods with CP risk-related movements, should be based on a cluster of movement features rather than single "key" features.…”

Section: Discussionmentioning

confidence: 93%

“…The ability of the CIMA model to predict CP was assessed by sensitivity, specificity, positive and negative predictive values (PPV and NPV) and area under receive operating characteristic curve (AUC). The performance of the CIMA model was compared with the performance of the observational GMA and cerebral imaging (cUS and MRI) [19] in addition to the automated method previously presented by our group using the variation of the spatial center of motion (C SD ) [12,30]. Kruskal-Wallis's test with the post hoc Wilcoxon rank-sum test including Bonferroni correction assessed the significance of the difference in the proportion of CP risk-related movements between the different FM categories assessed by the observational GMA.…”

Section: Statistics Of the Outcome Of The Cima Modelmentioning

confidence: 99%

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Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study

Ihlen

Støen

Boswell

et al. 2019

JCM

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Background: Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the Computer-based Infant Movement Assessment (CIMA) model, for clinically feasible early CP prediction based on infant video recordings. Methods: The CIMA model was designed to assess the proportion (%) of CP risk-related movements using a time-frequency decomposition of the movement trajectories of the infant's body parts. The CIMA model was developed and tested on video recordings from a cohort of 377 high-risk infants at 9-15 weeks corrected age to predict CP status and motor function (ambulatory vs. non-ambulatory) at mean 3.7 years age. The performance of the model was compared with results of the general movement 2 of 17 assessment (GMA) and neonatal imaging. Results: The CIMA model had sensitivity (92.7%) and specificity (81.6%), which was comparable to observational GMA or neonatal cerebral imaging for the prediction of CP. Infants later found to have non-ambulatory CP had significantly more CP risk-related movements (median: 92.8%, p = 0.02) compared with those with ambulatory CP (median: 72.7%). Conclusion: The CIMA model may be a clinically feasible alternative to observational GMA.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 93%

Section: Statistics Of the Outcome Of The Cima Modelmentioning

confidence: 99%

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Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study

Ihlen

Støen

Boswell

et al. 2019

JCM

Self Cite

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“…While the performance metrics look promising, the small sample size for this study of 30 high-risk infants can be questioned. Using recordings of 150 infants (10–15 weeks), Støen et al [ 89 ] elevated their work by incorporating sporadic FMs as well. These recently defined movements characterized by short FMs (1–3 s) with up to 1-min intermediate pauses and the absence of FMs were accounted for 48 of the infants by two certified observers.…”

Section: Methodology Of the Reviewed Approachesmentioning

confidence: 99%

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Irshad

Nisar

Gouverneur

et al. 2020

Sensors

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General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl’s assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

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“…In the last decade, scientists have developed systems for video-based assessments using optical flow which hold promise to be widely accessible as they can be implemented on mobile devices (Adde, Helbostad, Jensenius, Langaas, & Støen, 2013;Stahl et al, 2012;Støen et al, 2017). Such methods typically use frame differencing of a video to estimate movements by tracking the centroid of motion.…”

Section: Introductionmentioning

confidence: 99%

Computer vision to automatically assess infant neuromotor risk

Chambers

Seethapathi

Saluja

et al. 2019

Preprint

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An infant's risk of developing neuromotor impairment is primarily assessed through visual examination by specialized clinicians. Therefore, many infants at risk for impairment go undetected, particularly in under-resourced environments. There is thus a need to develop automated, clinical assessments based on quantitative measures from widely-available sources, such as video cameras. Here, we automatically extract body poses and movement kinematics from the videos of at-risk infants (N=19). For each infant, we calculate how much they deviate from a group of healthy infants (N=85 online videos) using Naïve Gaussian Bayesian Surprise. After pre-registering our Bayesian Surprise calculations, we find that infants that are at higher risk for impairments deviate considerably from the healthy group. Our simple method, provided as an open source toolkit, thus shows promise as the basis for an automated and low-cost assessment of risk based on video recordings.

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Computer-based video analysis identifies infants with absence of fidgety movements

Abstract: Background:

Cited by 37 publications

References 29 publications

Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study

Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study

AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review

Computer vision to automatically assess infant neuromotor risk

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