A team of clinicians, scientists, and engineers at Mount Sinai has developed an AI-powered tool to monitor infants in neonatal intensive care units (NICUs). By training a deep learning algorithm on video feeds of infants, the tool can accurately track movements and identify key neurological metrics. This innovative approach, detailed in the November 11 issue of Lancet’s eClinicalMedicine, offers a minimally invasive, scalable method for continuous neurological monitoring. By providing real-time insights into infant health, this AI-powered tool has the potential to revolutionize NICU care.
Every year, over 300,000 newborns in the US require intensive care. Infant alertness, a key indicator of neurological health, can deteriorate unexpectedly, leading to severe consequences. While continuous monitoring of heart and lung function is standard practice in NICUs, real-time neurological monitoring has remained elusive. Traditional methods, such as intermittent physical exams, are imprecise and may fail to detect subtle changes.
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The Mount Sinai team hypothesized that a computer vision method to track infant movement could predict neurologic changes in the NICU. “Pose AI” is a machine learning method that tracks anatomic landmarks from video data; it has revolutionized athletics and robotics.
The Mount Sinai team trained an AI algorithm on more than 16,938,000 seconds of video footage from a diverse group of 115 infants in the NICU at The Mount Sinai Hospital undergoing continuous video EEG monitoring. They demonstrated that Pose AI can accurately track infant landmarks from video data. They then used anatomic landmarks from the video data to predict two critical conditions—sedation and cerebral dysfunction—with high accuracy.
“Although many neonatal intensive care units contain video cameras, to date they do not apply deep learning to monitor patients,” said Felix Richter, MD, PhD, senior author of the paper and Instructor of Newborn Medicine in the Department of Pediatrics at Mount Sinai. “Our study shows that applying an AI algorithm to cameras that continuously monitor infants in the NICU is an effective way to detect neurologic changes early, potentially allowing for faster interventions and better outcomes.”
The research team was surprised by how well Pose AI worked across different lighting conditions (day vs. night vs. in babies receiving phototherapy) and from different angles. They were also surprised that their Pose AI movement index was associated with both gestational age and postnatal age.
“It’s important to note that this approach does not replace the physician and nursing assessments that are critical in the NICU. Rather, it augments these by providing a continuous readout that can then be acted on in a given clinical context,” explained Dr. Richter. “We envision a future system where cameras continuously monitor infants in the NICU, with AI providing a neuro-telemetry strip similar to heart rate or respiratory monitoring, with alert for changes in sedation levels or cerebral dysfunction. Clinicians could review videos and AI-generated insights when needed, offering an intuitive and easily interpretable tool for bedside care.”
