Usability and inter-rater reliability of the NeuroMotion app: A tool in General Movements Assessments

Svensson, Katarina; Örtqvist, Maria; Bos, Arend F.; Eliasson, Ann‐Christin; Sundelin, Heléne E.K.

doi:10.1016/j.ejpn.2021.05.006

Cited by 18 publications

(23 citation statements)

References 24 publications

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“…A recent review by Silva et al 18 highlighted the fact that adoption of automated CP prediction in clinical practice has been restricted because existing machine learning methods lack the predictive accuracy of the GMA. The feasibility of home-based smartphone recordings 22,[44][45][46][47] and associated infant motion tracking 37 may be combined with the proposed deep learning method to obtain a fully automated system for clinical decision support.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

et al. 2022

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Key Points Question What is the external validity of a deep learning–based method to predict cerebral palsy (CP) based on infants’ spontaneous movements at 9 to 18 weeks’ corrected age? Findings In this prognostic study of 557 infants with a high risk of perinatal brain injury, a deep learning–based method for early prediction of CP had sensitivity of 71%, specificity of 94%, positive predictive value of 68%, and negative predictive value of 95%. Prognosis of CP based on the deep learning–based method was associated with later functional level and CP subtype in children with CP. Meaning This study’s findings suggest that deep learning–based assessments could support early detection of CP in infants at high risk.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

et al. 2022

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“…This model has potential applications in the neonatal follow-up clinic. Specifically, smartphone applications are now available to record infant movements and upload them to a central database [ 52 , 53 , 54 ]. Thus, automated movement analysis could be used to pre-screen infants prior to a clinic visit and identify children who may need more intensive follow-up for motor impairment.…”

Section: Discussionmentioning

confidence: 99%

Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study

et al. 2022

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The General Movements Assessment requires extensive training. As an alternative, a novel automated movement analysis was developed and validated in preterm infants. Infants < 31 weeks’ gestational age or birthweight ≤ 1500 g evaluated at 3–5 months using the general movements assessment were included in this ambispective cohort study. The C-statistic, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for a predictive model. A total of 252 participants were included. The median gestational age and birthweight were 274/7 weeks (range 256/7–292/7 weeks) and 960 g (range 769–1215 g), respectively. There were 29 cases of cerebral palsy (11.5%) at 18–24 months, the majority of which (n = 22) were from the retrospective cohort. Mean velocity in the vertical direction, median, standard deviation, and minimum quantity of motion constituted the multivariable model used to predict cerebral palsy. Sensitivity, specificity, positive, and negative predictive values were 55%, 80%, 26%, and 93%, respectively. C-statistic indicated good fit (C = 0.74). A cluster of four variables describing quantity of motion and variability of motion was able to predict cerebral palsy with high specificity and negative predictive value. This technology may be useful for screening purposes in very preterm infants; although, the technology likely requires further validation in preterm and high-risk term populations.

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“…The Prechtl General Movements Assessment (GMA) is a commonly used video-based assessment of infant spontaneous movement, which can be easily and reliably recorded by parents, 9,10 with apps appearing in research settings to facilitate data capture, which are subsequently scored by trained clinicians. [11][12][13] Fidgety movements are defined as small amplitude, moderate speed, continuous movements seen in an infant's body between 9-and 20-weeks' corrected age (CA) when they are in an awake and alert state. Fidgety movements are absent if they are not seen at all through the duration that an infant has been filmed, sporadic if they are seen only momentarily and abnormal if they are exaggerated in speed and/or amplitude.…”

Section: Assessments Of Early Infant Neuromotor Function Such As Thementioning

confidence: 99%

Parent‐recorded videos of infant spontaneous movement: Comparisons at 3–4 months and relationships with 2‐year developmental outcomes in extremely preterm, extremely low birthweight and term‐born infants

Kwong

Doyle

Olsen

et al. 2022

Paediatric Perinatal Epid

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Background: Infants born extremely preterm (EP, <28-week gestational age) or extremely low birthweight (ELBW, <1000 g) are at risk of developmental delay and cerebral palsy (CP). The General Movements Assessment (GMA) and its extension, the Motor Optimality Score, revised (MOS-R) (assesses movement patterns and posture), may help to identify early delays.Objectives: To compare differences in the MOS-R scored from parent-recorded videos between infants born EP/ELBW and term-born infants, to determine relationships between the MOS-R and 2-year cognitive, language and motor outcomes and if any relationships differ between birth groups and the association of the GMA (fidgety) with CP.Methods: A geographical cohort (EP/ELBW and term-control infants) was assessed using the MOS-R inclusive of the GMA at 3-to 4-month corrected age (CA), and the Bayley Scales of Infant and Toddler Development, 3rd edition (Bayley-III) at 2-year CA. Differences in mean total MOS-R between groups, relationships between MOS-R and 2-year outcomes and relationships between GMA (fidgety) and CP in infants born EP/ELBW were estimated using linear/logistic regression.Results: Three hundred and twelve infants (147 EP/ELBW; 165 term) had complete MOS-R and Bayley-III assessments. Mean MOS-R was lower in infants born EP/ELBW than controls (mean difference −3.2, 95% confidence interval [CI] −4.2, −2.3). MOS-R was positively related to cognitive (β [regression coefficient] = 0.71, 95% CI 0.27, 1.15), language (β = 0.96, 95% CI 0.38, 1.54) and motor outcomes (β = .89, 95% CI 0.45, 1.34). There was little evidence for interaction effects between birth groups for any outcome. Absent/abnormal fidgety movements were related to CP in children born EP/ELBW (risk ratio 5.91, 95% CI 1.48, 23.7).Conclusions: Infants born EP/ELBW have lower MOS-R than infants born at term.

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Usability and inter-rater reliability of the NeuroMotion app: A tool in General Movements Assessments

Cited by 18 publications

References 24 publications

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study

Parent‐recorded videos of infant spontaneous movement: Comparisons at 3–4 months and relationships with 2‐year developmental outcomes in extremely preterm, extremely low birthweight and term‐born infants

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