Head Lag in Infancy: What Is It Telling Us?

Pineda, Roberta; Reynolds, L.; Seefeldt, Kristin S.; Hilton, Claudia; Rogers, Cynthia; Inder, Terrie E.

doi:10.5014/ajot.2016.017558

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…With the AI-aided autotracking process and the proposed data preprocessing, infants' skeletons can be captured on mobile videos without background and angle limitations and provide features for model recognition. A total of 42.4% of the preterm infants had head lag until term equilibrated age, and only 4% of the infants had the same symptoms at birth [8]. While head lag has proven to be a vital early warning sign and PTS is an easy and necessary neurologic examination, few studies have explored how technology could help identify it.…”

Section: Discussionmentioning

confidence: 99%

“…We chose PTS as an example for proof of concept because it is one of the essential items from HINE and an easy-to-use neurologic examination in clinics. Although head lag alone could not predict future NDI outcomes [8], infants with CP or neuromuscular disease usually showed early head lag [11]. Compared with low-risk infants, head lag was demonstrated as an early predictive biomarker of future communication delays and diagnosis of autism in children with autistic siblings [9,10].…”

Section: Limitationsmentioning

confidence: 99%

“…Among different motor skills, the development of gross motor domains for children dramatically improves after birth to one year old. Gross motor delay under one year of age may be the consequence of perinatal complications for high-risk infants, such as preterm birth [8] and early signs of neurobehavioral disorders [9,10]. In addition, DDs, such as cerebral palsy (CP) or neuromuscular disease, usually show early gross motor delay [11].…”

Section: Introductionmentioning

confidence: 99%

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Mobile Device-Based Video Screening for Infant Head Lag: An Exploratory Study

et al. 2023

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Introduction: Video-based automatic motion analysis has been employed to identify infant motor development delays. To overcome the limitations of lab-recorded images and training datasets, this study aimed to develop an artificial intelligence (AI) model using videos taken by mobile phone to assess infants’ motor skills. Methods: A total of 270 videos of 41 high-risk infants were taken by parents using a mobile device. Based on the Pull to Sit (PTS) levels from the Hammersmith Motor Evaluation, we set motor skills assessments. The videos included 84 level 0, 106 level 1, and 80 level 3 recordings. We used whole-body pose estimation and three-dimensional transformation with a fuzzy-based approach to develop an AI model. The model was trained with two types of vectors: whole-body skeleton and key points with domain knowledge. Results: The average accuracies of the whole-body skeleton and key point models for level 0 were 77.667% and 88.062%, respectively. The Area Under the ROC curve (AUC) of the whole-body skeleton and key point models for level 3 were 96.049% and 94.333% respectively. Conclusions: An AI model with minimal environmental restrictions can provide a family-centered developmental delay screen and enable the remote monitoring of infants requiring intervention.

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Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mobile Device-Based Video Screening for Infant Head Lag: An Exploratory Study

et al. 2023

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“…12 We found statistically significant differences in head lag and ventral suspension, which too align with other studies of infants born less than 30 weeks' gestation that showed persistence of head lag at term-equivalent age. 13…”

Section: Tone and Posturementioning

confidence: 99%

Comparison of the neurobehavioural profile of early‐preterm infants against term and late‐preterm infants using the Hammersmith neonatal neurological examination

Ong

Baral

Ereno

et al. 2022

J Paediatrics Child Health

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To compare the neurobehavioural profile of early-preterm infants (<32 weeks gestation) at term-corrected age (39 +0 -41 +6 weeks) versus late-preterm and full-term infants at similar term gestational ages. Methods: Early-preterm infants were assessed neurologically at term-corrected age using the Hammersmith neonatal neurological examination. The raw scores of the 34 Hammersmith neonatal neurological examination items were converted to optimality scores. Pairwise comparison of neurobehavioural patterns between early-preterm infants at term-corrected age versus late-preterm and full-term infants at similar gestational ages were made using independent sample t tests. Differences in optimality scores between the three groups were evaluated using one-way analysis of variance. Results: Sixty-eight early-preterm infants assessed at term-corrected age were compared against 75 late-preterm infants and 133 full-term infants. Mean total optimality scores (AEstandard deviation) of early-preterm, late-preterm and full-term infants at term-corrected age were 27.68 (AE3.97), 29.09 (AE2.45) and 31.58 (AE1.39), respectively (P < 0.001). The mean optimality score of early-preterm infants was significantly lower when compared pairwise with late-preterm infants and full-term infants with mean difference of À1.42 (P = 0.013) and À3.91 (P < 0.001), respectively. Conclusion:The neurobehavioural profile of early-preterm infants lags significantly behind those of late-preterm and full-term infants at termcorrected age. This study also provides reference raw and optimality scores for all items in the Hammersmith neonatal neurological examination for early-preterm infants in a predominantly Asian population.

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