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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).
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| NICU-Admitted Infants Undergoing Continuous Video Monitoring | Infants admitted to newborn services, including the neonatal intensive care unit (NICU), who meet eligibility criteria and undergo continuous, non-contact bedside video monitoring from enrollment until hospital discharge. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Continuous bedside video monitoring with AI anatomic landmark tracking for neurologic monitoring | Device | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| AI Anatomic Landmark Tracking Accuracy | 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. | At study completion, an average of 1 week. |
| Measure | Description | Time Frame |
|---|---|---|
| Movement Index - Encephalopathy measured by modified Sarnat exam | 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. | Through study completion, an average of 1 week. |
| Measure | Description | Time Frame |
|---|---|---|
| AI Anatomic Landmark Tracking - Post-Menstrual Age at Video | Assess performance of AI anatomic landmark tracking (median position error) across post-menstrual ages at the time of video recording. | At study completion, an average of 1 week. |
| AI Anatomic Landmark Tracking - Encephalopathy Status |
Inclusion Criteria:
Exclusion Criteria:
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Neonates of any sex, gestational age, demographic background, or health status admitted to newborn services, including the NICU, at a participating hospital. No diagnosis-specific criteria apply. Consent is provided by at least one parent or legally authorized representative aged 18 or older. Sites will make reasonable efforts to enroll a demographically diverse sample reflective of their local NICU populations, supporting prespecified subgroup analyses of AI performance consistency across gestational age, race/ethnicity, sex, and clinical condition. The first five participants at each site are excluded from primary and secondary endpoint analyses and serve as a technology familiarization cohort.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Saum Naderi, MA | Contact | 714-913-3641 | saum@artemisailabs.com | |
| Florian Richter, PhD | Contact | 773-312-3301 | florian@artemisailabs.com |
| Name | Affiliation | Role |
|---|---|---|
| Benjamin Glicksberg, PhD | Icahn School of Medicine at Mount Sinai | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Mount Sinai Hospital | New York | New York | 10029 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41532764 | Background | Feng R, Richter F, Mari E, Gleason A, Le C, Kellner CP, Shrivastava RK, Fields M, Rapoport BI, Bederson JB, Schadt EE, Glicksberg BS, Richter F, Dangayach NS. Artificial Intelligence Monitoring of Neurological Status From Patient Videos in the Neuroscience Intensive Care Unit. Neurosurgery. 2026 Jan 14. doi: 10.1227/neu.0000000000003899. Online ahead of print. | |
| 39764545 |
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Aggregate deidentified data and results will be shared. Individual participant video data will not be shared due to PHI concerns.
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| ID | Term |
|---|---|
| D020925 | Hypoxia-Ischemia, Brain |
| D001927 | Brain Diseases |
| ID | Term |
|---|---|
| D002545 | Brain Ischemia |
| D002561 | Cerebrovascular Disorders |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
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|
| Movement Index - N-PASS |
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. |
| Through study completion, an average of 1 week. |
| Movement Index - Sedative Exposure | Association between movement index and sedative exposure, a routinely collected clinical variable that influences neonatal arousal. | Through study completion, an average of 1 week. |
| Movement Index - Chronological Age at Video | Association between movement index and chronological age at video, a routinely collected clinical variable that influences neonatal arousal. | Through study completion, an average of 1 week. |
| Movement Index - Gestational age at birth | Association between the movement index and gestational age at birth, a routinely collected clinical variable that influences neonatal arousal. | Through study completion, an average of 1 week. |
| Movement Index - Sleep state | Association between the movement index and sleep state, a routinely collected clinical variable that influences neonatal arousal. | Through study completion, an average of 1 week. |
| Movement Index - EEG evidence of cerebral dysfunction | Association between the movement index and, if obtained as part of routine clinical care, EEG evidence of cerebral dysfunction (a biomarker of encephalopathy). | Through study completion, an average of 1 week. |
Assess performance of AI anatomic landmark tracking (median position error) across encephalopathy statuses. |
| At study completion, an average of 1 week. |
| AI Anatomic Landmark Tracking - Caregiver-reported Race/Ethnicity | Assess performance of AI anatomic landmark tracking (median position error) across caregiver-reported race/ethnicity. | At study completion, an average of 1 week. |
| AI Anatomic Landmark Tracking - Sex | Assess performance of AI anatomic landmark tracking (median position error) and sex. | At study completion, an average of 1 week. |
| AI Anatomic Landmark Tracking - Lighting Conditions | Assess performance of AI anatomic landmark tracking (median position error) and lighting conditions (phototherapy, lights on/off, time of day). | At study completion, an average of 1 week. |
| Parent and Provider Feedback | Brief structured surveys administered to parents and clinical providers to assess acceptability, usability, and perceived burden of the bedside video monitoring system. Findings will inform system design refinements and support future bedside adoption and regulatory human factors documentation. | At baseline and study completion (Day 1 - Day 7, on average). |
| Weill Cornell Medicine / NewYork-Presbyterian Hospital | New York | New York | 10065 | United States |
|
| Gleason A, Richter F, Beller N, Arivazhagan N, Feng R, Holmes E, Glicksberg BS, Morton SU, La Vega-Talbott M, Fields M, Guttmann K, Nadkarni GN, Richter F. Detection of neurologic changes in critically ill infants using deep learning on video data: a retrospective single center cohort study. EClinicalMedicine. 2024 Nov 11;78:102919. doi: 10.1016/j.eclinm.2024.102919. eCollection 2024 Dec. |
| D002534 | Hypoxia, Brain |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D000860 | Hypoxia |
| D012818 | Signs and Symptoms, Respiratory |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |