Spontaneous movements in the newborns: a tool of quantitative video analysis of preterm babies

Tacchino, Chiara; Impagliazzo, M.; Maggi, Erika; Bertamino, Marta; Blanchi, I.; Campone, Francesco; Durand, Paola; Fato, Marco; Giannoni, Psiche; Iandolo, Riccardo; Morasso, Pietro; Moretti, Paolo; Ramenghi, Luca A.; Shima, Keisuke; Shimatani, Koji; Tsuji, Takao; Uccella, Sarà; Zanardi, Nicolò; Casadio, Maura

doi:10.1016/j.cmpb.2020.105838

Cited by 14 publications

(7 citation statements)

References 45 publications

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“…The primary goal of the present study was to examine whether bodily movement patterns during early infancy are predictive of the ASD risk, which becomes evident later in the development in a prospective design. To achieve this goal, we video-recorded spontaneous bodily movements in 4-month-old infants and quantified their features using a markerless system for infant movement evaluation, which is a modification of our conventional approach [30][31][32][33] . We focused on bodily movement patterns at 4-month-old mainly based on the following reasons.…”

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confidence: 99%

Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants

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Iijima

Furui

et al. 2022

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Early intervention is now considered the core treatment strategy for autism spectrum disorders (ASD). Thus, it is of significant clinical importance to establish a screening tool for the early detection of ASD in infants. To achieve this goal, in a longitudinal design, we analyzed spontaneous bodily movements of 4-month-old infants from general population and assessed their ASD-like behaviors at 18 months of age. A total of 26 movement features were calculated from video-recorded bodily movements of infants at 4 months of age. Their risk of ASD was assessed at 18 months of age with the Modified Checklist for Autism in Toddlerhood, a widely used screening questionnaire. Infants at high risk for ASD at 18 months of age exhibited less rhythmic and weaker bodily movement patterns at 4 months of age than low-risk infants. When the observed bodily movement patterns were submitted to a machine learning-based analysis, linear and non-linear classifiers successfully predicted ASD-like behavior at 18 months of age based on the bodily movement patterns at 4 months of age, at the level acceptable for practical use. This study analyzed the relationship between spontaneous bodily movements at 4 months of age and the ASD risk at 18 months of age. Experimental results suggested the utility of the proposed method for the early screening of infants at risk for ASD. We revealed that the signs of ASD risk could be detected as early as 4 months after birth, by focusing on the infant’s spontaneous bodily movements.

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confidence: 99%

Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants

Doi

Iijima

Furui

et al. 2022

Sci Rep

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“…A total of 262 full-term newborns from the NICU were analyzed. Most newborns who remained in the NICU were boys (52%); white (61.1%); gestational age of 39.1±1.3 weeks (mean±SD); birth weight of 3,191±539 grams; vaginal childbirth (55.3%); the 5-minute Apgar score presented a median (P25-P75) of nine (8)(9); mean ± SD of maternal age was of 30.1±12.8 years; 48.9% of the mothers were white; and median number of consultations (P25-P75) of 7 (4)(5)(6)(7)(8)(9)(10)(11).…”

Section: Resultsmentioning

confidence: 99%

“…When seeking "full-term newborns" and "neonatal intensive care unit," only 246 were found. That is, there are almost four times fewer studies investigating full-term babies in NICU 6 , and it is not because they are hospitalized in smaller numbers.…”

Section: Introductionmentioning

confidence: 99%

Why we should care about full-term infants admitted to a neonatal intensive care unit

et al. 2022

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This study aims to analyze why we should care about full-term newborns admitted to a neonatal intensive care unit. This is a documented, descriptive, and retrospective study of 262 full-term newborns. Variables used: newborns’ characteristics; main diagnosis, length of stay, follow-up by a multidisciplinary team; post-discharge referral. Most newborns were boys (52%), had a 5-minute Apgar score of nine, and most newborns and their mothers were white (61.1% and 48.9% respectively). Respiratory dysfunction was the main diagnosis (28.8%). Length of stay was eight days. There was a significant difference regarding length of stay (p=0.013), in which those with cardiorespiratory and other diseases stayed less time compared to those with malformation or maternal diseases. The social service was the most sought (81.2%) service, whereas physical therapy the least sought (18%). Newborns with higher weight were hospitalized for less time. Those that underwent physical therapy had longer stay (p<0.001). Main outcome was hospital discharge (68.7%) and referrals to the Basic Health Unit (57%). This study outcomes indicated newborns with less severe conditions, low number of specific studies for the full-term population, other diagnoses that refer to non-intensive care.

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“…However, with the exception of one other group [ 15 ], these studies have used convenience samples rather than consecutively recruited patients and have small sample sizes with which they have validated their technology. For example, one study’s discrimination of abnormal movements in infants with abnormal GMA clinically was lower because of the limited number of cases available [ 16 ]. Several technologies have relied on direct sensing (wearable techniques), which can be cumbersome and affect the infant’s movement [ 12 , 17 , 18 , 19 , 20 , 21 ].…”

Section: Introductionmentioning

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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Spontaneous movements in the newborns: a tool of quantitative video analysis of preterm babies

Cited by 14 publications

References 45 publications

Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants

Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants

Why we should care about full-term infants admitted to a neonatal intensive care unit

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

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