Hypoxic-Ischemic Encephalopathy, Neonatal Encephalopathy, Sedation, Sleep
Conditions
Keywords
Pose AI, Neonatal, Neonate, AI, Video AI, artificial intelligence, computer vision, NICU, Encephalopathy, Sedation, neonatal monitoring, movement analysis, pose estimation, machine learning, neurological assessment, spontaneous movement
Brief summary
NeoNOVA is a multi-site, prospective, single-arm, silent observational study to determine: among (Population) infants admitted to newborn services during their inpatient hospital stay, whether (Intervention) continuous bedside non-contact high definition video running real-time AI analysis of anatomic landmarks and movement, (Comparison) compared against human-labeled video frames and standardized clinical exams, will (Outcome) accurately localize infant anatomic landmarks (primary objective; outcome median position error in pixels) and demonstrate a statistically significant association between a video-derived movement index and clinical measures of patient neurological exams (secondary objective; outcomes N-PASS and modified Sarnat exams).
Detailed description
To fill this critical gap in neonatal care, the investigators developed and validated NeoPose, a low-cost, non-invasive, computer vision digital health tool to continuously monitor infants using real time video streams. NeoPose uses Pose Artificial Intelligence (AI) for an explainable approach to measure, quantify, and analyze infant movement. From the vectorized movement, investigators can accurately confirm the presence of encephalopathy and quantify the degree of sedation. The explainable AI platform enables continuous neuromonitoring with AI-driven alerts, suspicious event replay, movement comparisons, and training on a vast dataset of normal and abnormal infant movements far beyond what any provider could witness. The Neonatal Neurological Observation with Video AI (NeoNOVA) study is a multi-site, prospective, single-arm, pragmatic, silent observational study to evaluate the performance of NeoPose and AI-derived insights in real world settings. NeoNOVA will deploy a bedside video monitoring system (ArtemisAI Platform) that continuously, passively video records the subject from enrollment to discharge. The study will prospectively validate the AI system's tracking accuracy against ground-truth human-labeled video frames (primary objective; outcome median position error in pixels), will evaluate the association between a video-derived movement index and standardized bedside assessments of encephalopathy, pain, and sedation (secondary objective; outcomes N-PASS and modified Sarnat scales), and will support hypothesis-generating research on novel video prediction algorithms for outcomes like sepsis and need for respiratory support (tertiary objective). The study operates in "silent mode," where AI outputs are not shown to the patient's clinical team. Findings are intended to support a structured clinical evidence generation plan for a Software as a Medical Device (SaMD) designed for continuous, non-contact neurological monitoring in the NICU.
Interventions
DEVICEContinuous bedside video monitoring with AI anatomic landmark tracking for neurologic monitoring
A non-contact, passive bedside video recording system is mounted adjacent to the infant's crib or incubator. The device continuously captures video data from enrollment to hospital discharge or withdrawal. The device runs AI models to track infant anatomic landmarks and calculate a continuous movement index. The trial runs in "silent mode," where AI outputs are not shown to the patient's clinical team and do not influence care.
Sponsors
Artemis AI Labs
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
* Signed and dated informed consent from at least one parent or legally authorized representative (LAR) who is at least 18 years old. * Parent/LAR expresses willingness to comply with study procedures for the duration of the infant's hospital stay. * Infant of any sex (including intersex/undetermined) admitted to newborn services (including the NICU) at a participating hospital.
Exclusion criteria
* Parents or LAR unable to provide informed consent or are under the age of 18. * Non-viable neonates
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AI Anatomic Landmark Tracking Accuracy | At study completion, an average of 1 week. | The primary endpoint is analytical performance of the AI pose estimation system, quantified as median position error (in pixels) between AI-predicted and human-labeled anatomic landmark positions extracted from continuous bedside video. Success is defined as median position error less than typical human inter-rater variability. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Movement Index - Encephalopathy measured by modified Sarnat exam | Through study completion, an average of 1 week. | Association between a video-derived movement index and encephalopathy classification of severity from the modified Sarnat exam score, a bedside neurological exam assessed by trained clinical staff. |
| Movement Index - N-PASS | Through study completion, an average of 1 week. | Association between a video-derived movement index and Neonatal Pain, Agitation, and Sedation Scale (N-PASS) score (ordinal outcome), a bedside neurological exam measuring pain/sedation and assessed by trained clinical staff. |
| Movement Index - Sedative Exposure | Through study completion, an average of 1 week. | Association between movement index and sedative exposure, a routinely collected clinical variable that influences neonatal arousal. |
| Movement Index - Chronological Age at Video | Through study completion, an average of 1 week. | Association between movement index and chronological age at video, a routinely collected clinical variable that influences neonatal arousal. |
| Movement Index - Gestational age at birth | Through study completion, an average of 1 week. | Association between the movement index and gestational age at birth, a routinely collected clinical variable that influences neonatal arousal. |
| Movement Index - Sleep state | Through study completion, an average of 1 week. | Association between the movement index and sleep state, a routinely collected clinical variable that influences neonatal arousal. |
| Movement Index - EEG evidence of cerebral dysfunction | Through study completion, an average of 1 week. | Association between the movement index and, if obtained as part of routine clinical care, EEG evidence of cerebral dysfunction (a biomarker of encephalopathy). |
Countries
United States
Contacts
CONTACTSaum Naderi, MA
CONTACTFlorian Richter, PhD
PRINCIPAL_INVESTIGATORBenjamin Glicksberg, PhD
Icahn School of Medicine at Mount Sinai
Outcome results
None listed