High-risk infants in the neonatal intensive care unit often encounter the problems with hemodynamic instability, and the poor blood circulation may cause shock or other sequelae. But the appearance of shock is not easy to be noticed in the initial stage, and most of the clinical judgments are subjectively dependent on the experienced physicians. Therefore, how to effectively evaluate the neonatal blood circulation state is important for the treatment in time. Although some instruments, such as laser Doppler flow meter, can estimate the information of blood flow, there is still lack of monitoring systems to evaluate the neonatal blood circulation directly. Based on the technique of near-infrared spectroscopy, an intelligent neonatal blood perfusion assessment system was proposed in this study, to monitor the changes of hemoglobin concentration and tissue oxygen saturation simultaneously and further estimate the neonatal blood perfusion. Several indexes were defined from the changes of hemoglobin parameters under applying and relaxing pressure to obtain the neonatal perfusion information. Moreover, the neural network-based classifier was also used to effectively classify the groups with different blood perfusion states. From the experimental results, the difference between the groups with different blood perfusion states could exactly be reflected on several defined indexes and could be effectively recognized by using the technique of neural network. Clinical and Translational Impact Statement-An intelligent neonatal blood perfusion assessment system was proposed to monitor the changes of hemoglobin concentration and tissue oxygen saturation simultaneously and further estimate the neonatal blood perfusion (Category: Preclinical Research).
Keywords: Neonatal intensive care unit; blood perfusion; near-infrared spectroscopy; neural network; shock.
© 2025 The Authors.