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- Aizawa, CYP, et al.
(författare)
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Neurodevelopment in the third year of life in children with antenatal ZIKV-exposure
- 2021
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Ingår i: Revista de saude publica. - : Universidade de Sao Paulo, Agencia USP de Gestao da Informacao Academica (AGUIA). - 1518-8787 .- 0034-8910. ; 55, s. 15-
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Tidskriftsartikel (refereegranskat)abstract
- We report cognitive, language and motor neurodevelopment, assessed by the Bayley-III test, in 31 non-microcephalic children at age 3 with PCR-confirmed maternal Zika virus exposure (Rio de Janeiro, 2015–2016). Most children had average neurodevelopmental scores, however, 8 children (26%) presented delay in some domain. Language was the most affected: 7 children (22.6%) had a delay in this domain (2 presenting severe delay). Moderate delay was detected in the cognitive (3.2%) and motor (10%) domains. Maternal illness in the third trimester of pregnancy and later gestational age at birth were associated with higher Bayley-III scores. Zika-exposed children require long-term follow-up until school age.
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- Martinez, VF, et al.
(författare)
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Neuromotor repertoires in infants exposed to maternal COVID-19 during pregnancy: a cohort study
- 2023
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Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 13:1, s. e069194-
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Tidskriftsartikel (refereegranskat)abstract
- To evaluate neuromotor repertoires and developmental milestones in infants exposed to antenatal COVID-19.DesignLongitudinal cohort study.SettingHospital-based study in Los Angeles, USA and Rio de Janeiro, Brazil between March 2020 and December 2021.ParticipantsInfants born to mothers with COVID-19 during pregnancy and prepandemic control infants from the Graz University Database.InterventionsGeneral movement assessment (GMA) videos between 3 and 5 months post-term age were collected and clinical assessments/developmental milestones evaluated at 6–8 months of age. Cases were matched by gestational age, gender and post-term age to prepandemic neurotypical unexposed controls from the database.Main outcome measuresMotor Optimality Scores Revised (MOS-R) at 3–5 months. Presence of developmental delay (DD) at 6–8 months.Results239 infants were enrolled; 124 cases (83 in the USA/41 in Brazil) and 115 controls. GMA was assessed in 115 cases and 115 controls; 25% were preterm. Median MOS-R in cases was 23 (IQR 21–24, range 9–28) vs 25 (IQR 24–26, range 20–28) in controls, p<0.001. Sixteen infants (14%) had MOS-R scores <20 vs zero controls, p<0.001. At 6–8 months, 13 of 109 case infants (12%) failed to attain developmental milestones; all 115 control infants had normal development. The timing of maternal infection in pregnancy (first, second or third trimester) or COVID-19 disease severity (NIH categories asymptomatic, mild/moderate or severe/critical) was not associated with suboptimal MOS-R or DD. Maternal fever in pregnancy was associated with DD (OR 3.7; 95% CI 1.12 to 12.60) but not suboptimal MOS-R (OR 0.25; 95% CI 0.04 to 0.96).ConclusionsCompared with prepandemic controls, infants exposed to antenatal COVID-19 more frequently had suboptimal neuromotor development.
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- Reich, S, et al.
(författare)
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Novel AI driven approach to classify infant motor functions
- 2021
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1, s. 9888-
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Tidskriftsartikel (refereegranskat)abstract
- The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA). This study proposes a novel machine learning algorithm to detect an age-specific movement pattern, the fidgety movements (FMs), in a prospectively collected sample of typically developing infants. Participants were recorded using a passive, single camera RGB video stream. The dataset of 2800 five-second snippets was annotated by two well-trained and experienced GMA assessors, with excellent inter- and intra-rater reliabilities. Using OpenPose, the infant full pose was recovered from the video stream in the form of a 25-points skeleton. This skeleton was used as input vector for a shallow multilayer neural network (SMNN). An ablation study was performed to justify the network’s architecture and hyperparameters. We show for the first time that the SMNN is sufficient to discriminate fidgety from non-fidgety movements in a sample of age-specific typical movements with a classification accuracy of 88%. The computer-based solutions will complement original GMA to consistently perform accurate and efficient screening and diagnosis that may become universally accessible in daily clinical practice in the future.
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