Preterm infants' spontaneous motility is a valuable diagnostic and prognostic index of motor and cognitive impairments. Despite being recognized as crucial, preterm infant's movement assessment is mostly based on clinicians' visual inspection. The aim of this work is to present a 2D dense convolutional neural network (denseCNN) to detect preterm infant's joints in depth images acquired in neonatal intensive care units. The denseCNN allows to improve the performance of our previous model in the detection of joints and joint connections, reaching a median recall value equal to 0.839. With a view to monitor preterm infants in a scenario where computational resources are scarce, we tested the architecture on a mid-range laptop. The prediction occurs in real-time (0.014 s per image), opening up the possibility of integrating such monitoring system in a domestic environment.
Improving Preterm Infants' Joint Detection in Depth Images Via Dense Convolutional Neural Networks / Migliorelli, L.; Frontoni, E.; Appugliese, S.; Cannata, G. P.; Carnielli, V.; Moccia, S.. - 2021:(2021), pp. 3013-3016. (Intervento presentato al convegno 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021 tenutosi a mex nel 2021) [10.1109/EMBC46164.2021.9630407].
Improving Preterm Infants' Joint Detection in Depth Images Via Dense Convolutional Neural Networks
Carnielli V.;
2021-01-01
Abstract
Preterm infants' spontaneous motility is a valuable diagnostic and prognostic index of motor and cognitive impairments. Despite being recognized as crucial, preterm infant's movement assessment is mostly based on clinicians' visual inspection. The aim of this work is to present a 2D dense convolutional neural network (denseCNN) to detect preterm infant's joints in depth images acquired in neonatal intensive care units. The denseCNN allows to improve the performance of our previous model in the detection of joints and joint connections, reaching a median recall value equal to 0.839. With a view to monitor preterm infants in a scenario where computational resources are scarce, we tested the architecture on a mid-range laptop. The prediction occurs in real-time (0.014 s per image), opening up the possibility of integrating such monitoring system in a domestic environment.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.