Neonatal Neurological Observation With Video AI

NCT07628829 | Not Yet Recruiting FDA Device

• 1 other identifier: STUDY-25-01036
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 2

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).

Conditions

Neonatal Encephalopathy; Hypoxic-Ischemic Encephalopathy; Sedation; Sleep

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

Outcome Measures

Primary Outcomes (1)

• 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.

Secondary Outcomes (7)

• Movement Index - Encephalopathy measured by modified Sarnat exam

  Through study completion, an average of 1 week.

• Movement Index - N-PASS

  Through study completion, an average of 1 week.

• Movement Index - Sedative Exposure

  Through study completion, an average of 1 week.

• Movement Index - Chronological Age at Video

  Through study completion, an average of 1 week.

• Movement Index - Gestational age at birth

  Through study completion, an average of 1 week.

Other Outcomes (6)

• AI Anatomic Landmark Tracking - Post-Menstrual Age at Video

  At study completion, an average of 1 week.

• AI Anatomic Landmark Tracking - Encephalopathy Status

  At study completion, an average of 1 week.

• AI Anatomic Landmark Tracking - Caregiver-reported Race/Ethnicity

  At study completion, an average of 1 week.

Study Arms (1)

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.

Device: Continuous bedside video monitoring with AI anatomic landmark tracking for neurologic monitoring

Interventions

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.

NICU-Admitted Infants Undergoing Continuous Video Monitoring

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• 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.

You may not qualify if:

• Parents or LAR unable to provide informed consent or are under the age of 18.
• Non-viable neonates

Sponsors & Collaborators

• Artemis AI Labs: lead

Study Sites (2)

Mount Sinai Hospital

New York, New York, 10029, United States

Location

Weill Cornell Medicine / NewYork-Presbyterian Hospital

New York, New York, 10065, United States

Location

Related Publications (2)

• 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.

  PMID: 41532764 BACKGROUND

• 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.

  PMID: 39764545 BACKGROUND

MeSH Terms

Conditions

Hypoxia-Ischemia, Brain; Brain Diseases

Condition Hierarchy (Ancestors)

Brain Ischemia; Cerebrovascular Disorders; Central Nervous System Diseases; Nervous System Diseases; Hypoxia, Brain; Vascular Diseases; Cardiovascular Diseases; Hypoxia; Signs and Symptoms, Respiratory; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Benjamin Glicksberg, PhD

  Icahn School of Medicine at Mount Sinai

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Saum Naderi, MA

CONTACT

714-913-3641; saum@artemisailabs.com

Florian Richter, PhD

CONTACT

773-312-3301; florian@artemisailabs.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 26, 2026
• First Posted: June 5, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): May 31, 2027
• Study Completion (Estimated): May 31, 2029
• Last Updated: June 8, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

US (2)