Improving Adherence to Neonatal Resuscitation Using Machine Learning at Quality Improvement Approach (Pre-MALA)

High quality neonatal resuscitation is a key to save newborn lives, prevent brain injury and optimize child development, yet the quality of care remains far below standards. In this project, we investigate the use of video filming of neonatal resuscitation (source of data) to develop a machine learning application which automatically detects neonatal resuscitation activity. This artificial intelligence system will assist to standardize resuscitation in neonates requiring assisted ventilation on the resuscitation table. The MAchine Learning Application (MALA) installed in a tablet mounted on the resuscitation table detects the baby’s crying (sound), breathing (chest movement) and health worker’s resuscitation action (stimulation, suctioning and bag-and-mask ventilation), and provides real-time feedback (reminder) on steps of resuscitation.The real-time feedback will be in the form of audio and visual signals from the tablet during resuscitation. Following the completion of resuscitation, MALA provides a summary feedback on the resuscitation steps followed as per the resuscitation guideline.