TUESDAY, Nov. 19, 2024 (HealthDay News) — Deep learning with pose artificial intelligence (AI) may offer a scalable, minimally invasive method for neuro-telemetry in the neonatal intensive care unit (NICU), according to a study published online Nov. 11 in eClinicalMedicine.
Alec Gleason, from the Albert Einstein College of Medicine in New York City, and colleagues assessed whether an AI-based computer vision method to track infant movement could predict neurologic changes in the NICU. The analysis included video data linked to electroencephalograms (video-EEG; 282,301 video minutes) from 115 infants with corrected age <1 year at four urban NICUs from Feb. 1, 2021, to Dec. 31, 2022.
The researchers found that infant pose was accurately predicted in cross-validation, held-out frames, and held-out infants with respective receiver operating characteristic area under the curves (ROC-AUCs) of 0.94, 0.83, and 0.89. After accounting for age, which was associated with median movement increases, median movement was lower with sedative medications and in infants with cerebral dysfunction. Sedation prediction had high performance on cross-validation, held-out intervals, and held-out infants (ROC-AUCs, 0.90, 0.91, and 0.87, respectively). Prediction of cerebral dysfunction also had high performance (ROC-AUCs, 0.91, 0.90, and 0.76, respectively).
“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,” senior author Felix Richter, M.D., Ph.D., from the Icahn School of Medicine at Mount Sinai in New York City, said in a statement. “Clinicians could review videos and AI-generated insights when needed, offering an intuitive and easily interpretable tool for bedside care.”
Several authors are named inventors of Pose AI to monitor neonatal mental status and have disclosed filing a provisional patent application.
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