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A team of clinicians, scientists, and engineers at Mount Sinai Clinical Intelligence Center trained a deep learning pose-recognition algorithm on video feeds of infants in the neonatal intensive care unit (NICU) to accurately track their movements and identify key neurologic metrics.
According to the team, the findings from this new artificial intelligence (AI)-based tool, published on November 11 in Lancet's eClinicalMedicine, could lead to a minimally invasive, scalable method for continuous neurologic monitoring in NICUs. The tool aims to provide critical real-time insights into infant health that have not been possible before.
Every year, a considerable number of infants are admitted to NICUs across the world. Infant alertness is considered the most sensitive piece of the neurologic exam, reflecting integrity throughout the central nervous system. Neurologic deterioration in NICUs can happen unexpectedly and has devastating consequences.
However, unlike cardiorespiratory telemetry, which continuously monitors babies' heart and lung function in the NICU, neurotelemetry has remained elusive in most NICUs despite decades of work in electroencephalography (EEG) and specialized neuro-NICUs. Neurologic status is evaluated intermittently, using imprecise physical exams that may miss subacute changes.
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 significantly advanced athletics and robotics.
The Mount Sinai team trained an AI algorithm on more than 16,938,000 seconds of video footage from 115 diverse infants in the NICU at 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 and postnatal age.
It's important to note that this approach does not replace the critical physician and nursing assessments in the NICU. Rather, it augments these by providing a continuous readout that can 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."
The team noted the study's limitations, including that the AI models were trained on data collected at a single institution. This means that this algorithm and neurologic predictions need to be evaluated on video data from other institutions and video cameras. The research team plans to test this technology in additional NICUs and develop clinical trials that will assess its impact on care. They are also exploring its application to other neurological conditions and expanding its use to adult populations.