Commentary on soft-electronic sensor network in PNAS
It’s amazing that just from observing the movements of infants you can predict the chance of later neurological deficits. Prof. John Rogers’ group used a soft-electronic sensor network for such prediction. Please read my and Prof. Zhenan Bao’s commentary here: ✨
Paper title: A soft-electronic sensor network tracks neuromotor development in infants
Abstract: The brain coordinates the body’s movements through the central nervous system (CNS). Hence, movement behaviors in infants reveal valuable information regarding their developing CNS. In infants, spontaneous movements often referred to as general movements (GMs) are an indicator of later neurological deficits. GMs are automatic, are complex, occur frequently, and can be observed accurately from early fetal life to 6 mo of age. Early observation and assessment of atypical GMs open up the possibility of therapeutic intervention in infants and rely on the neuroplasticity of the brain to avert potential negative outcomes. Qualitative and quantitative monitoring of GMs currently requires clinical tests, medical history, video monitoring, and medical experts. All these are time and resource intensive; therefore, they are not available to the wider population. In PNAS, Jeong et al. demonstrate an artificial intelligence-enabled soft-electronic sensor network that monitors movements in infants for predicting later neurological deficits.
Publication:
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A soft-electronic sensor network tracks neuromotor development in infants Proceedings of the National Academy of Sciences of the United States of America, 2021 118, 46.The brain coordinates the body’s movements through the central nervous system (CNS). Hence, movement behaviors in infants reveal valuable information regarding their developing CNS. In infants, spontaneous movements often referred to as general movements (GMs) are an indicator of later neurological deficits. GMs are automatic, are complex, occur frequently, and can be observed accurately from early fetal life to 6 mo of age. Early observation and assessment of atypical GMs open up the possibility of therapeutic intervention in infants and rely on the neuroplasticity of the brain to avert potential negative outcomes. Qualitative and quantitative monitoring of GMs currently requires clinical tests, medical history, video monitoring, and medical experts. All these are time and resource intensive; therefore, they are not available to the wider population. In PNAS, Jeong et al. demonstrate an artificial intelligence-enabled soft-electronic sensor network that monitors movements in infants for predicting later neurological deficits.
@article{khan2021soft, title = {A soft-electronic sensor network tracks neuromotor development in infants}, author = {Khan, Yasser and Bao, Zhenan}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {118}, number = {46}, pages = {e2116943118}, year = {2021}, doi = {10.1073/pnas.2116943118}, thumbnail = {khan2021soft.png}, url = {http://dx.doi.org/10.1073/pnas.2116943118}, pdf = {khan2021soft.pdf} }