Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study.
Alec Gleason, Florian Richter, Nathalia Beller...
https://pubmed.ncbi.nlm.nih.gov/39764545/Actively Recruiting
Led by Artemis AI Labs · Updated on 2026-07-15
200
Participants Needed
2
Research Sites
104 weeks
Total Duration
Researchers are evaluating a new video-based artificial intelligence AI system called NeoPose to continuously monitor neurological health in newborn infants admitted to hospital newborn services, including the neonatal intensive care unit NICU. This observational study aims to assess how accurately the AI can identify infant anatomic landmarks compared to human labeling and examine associations between AI-derived movement measures and clinical neurological exams. The study also explores AI predictions for outcomes like sepsis and respiratory support needs. Participants will be continuously monitored using a non-contact bedside video recording device mounted near their crib or incubator from enrollment until hospital discharge. The device captures real-time video data running AI models silently without displaying results to clinical staff. The study is conducted at multiple sites and collects data throughout the infants hospital stay. During the study, infants will undergo continuous video monitoring while researchers compare AI tracking results against human-labeled video frames and standardized neurological assessments such as the modified Sarnat and N-PASS exams. The study measures include AI tracking accuracy and movement indexes related to encephalopathy, sedation, sleep, and brain function. Participation lasts for the entire hospital stay, with continuous passive data collection and no alteration to clinical care.
CONDITIONS
Neonatal Neurological Observation With Video AI
S
Saum Naderi, MA
F
Florian Richter, PhD
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Artemis AI Labs
Lead Sponsor
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Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
Duration - From enrollment until hospital discharge
Participants undergo continuous bedside video monitoring using a non-contact device from enrollment until hospital discharge. The AI system silently records and analyzes infant movement without affecting clinical care.
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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Alec Gleason, Florian Richter, Nathalia Beller...
https://pubmed.ncbi.nlm.nih.gov/39764545/Rui Feng, Florian Richter, Elizabeth Mari...
https://pubmed.ncbi.nlm.nih.gov/41532764/Artemis AI Labs
Lead Sponsor