Search Results (2)

Breastfeeding is widely recognized as the gold standard for infant nutrition, benefitting infants’ gastrointestinal tracts. Stool analysis helps in understanding pediatric gastrointestinal health, but the effectiveness of automated fecal consistency evaluation by parents of breastfeeding infants has not been investigated. Photographs of one-month-old infants’ feces on diapers were taken via a smartphone app and independently categorized by Artificial Intelligence (AI), parents, and researchers. The accuracy of the evaluations of the AI and the parents was assessed and compared. The factors contributing to assessment bias and app user characteristics were also explored. A total of 98 mother–infant pairs contributed 905 fecal images, 94.0% of which were identified as loose feces. AI and standard scores agreed in 95.8% of cases, demonstrating good agreement (intraclass correlation coefficient (ICC) = 0.782, Kendall’s coefficient of concordance W (Kendall’s W) = 0.840, Kendall’s tau = 0.690), whereas only 66.9% of parental scores agreed with standard scores, demonstrating low agreement (ICC = 0.070, Kendall’s W = 0.523, Kendall’s tau = 0.058). The more often a mother had one or more of the following characteristics, unemployment, education level of junior college or below, cesarean section, and risk for postpartum depression (PPD), the more her appraisal tended to be inaccurate (p < 0.05). Each point increase in the Edinburgh Postnatal Depression Scale (EPDS) score increased the deviation by 0.023 points (p < 0.05), which was significant only in employed or cesarean section mothers (p < 0.05). An AI-based stool evaluation service has the potential to assist mothers in assessing infant stool consistency by providing an accurate, automated, and objective assessment, thereby helping to monitor and ensure the well-being of infants. Full article

Neonatal jaundice refers to the abnormality of bilirubin metabolism for newborns, and wearable transcutaneous bilirubin meters for real-time measuring the bilirubin concentration is an insistent demand for the babies’ parents and doctors. In this paper, a self-powered wearable biosensor in a baby diaper for real-time monitoring neonatal jaundice has been realized by the hydrovoltaic-biosensing coupling effect of ZnO nanoarray. Without external power supply, the system can work independently, and the hydrovoltaic output can be treated as both the power source and biosensing signal. The working mechanism is that the hydrovoltaic output arises from the urine flowing on ZnO nanoarray and the enzymatic reaction on the surface can influence the output. The sensing information can be transmitted through a wireless transmitter, and thus the parents and doctors can treat the neonatal jaundice of babies in time. This work can potentially promote the development of next generation of biosensors and physiological monitoring system, and expand the scope of self-powered technique and smart healthcare area. Full article