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NCT Number: NCT07628829

Neonatal Neurological Observation With Video AI

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).

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Key information

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Primary outcomes

  1. AI Anatomic Landmark Tracking Accuracy

    Time frame: 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 outcomes

  1. Movement Index - Encephalopathy measured by modified Sarnat exam

    Time frame: 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.

  2. Movement Index - N-PASS

    Time frame: 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.

  3. Movement Index - Sedative Exposure

    Time frame: Through study completion, an average of 1 week.

    Association between movement index and sedative exposure, a routinely collected clinical variable that influences neonatal arousal.

  4. Movement Index - Chronological Age at Video

    Time frame: 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.

  5. Movement Index - Gestational age at birth

    Time frame: 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.

  6. Movement Index - Sleep state

    Time frame: 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.

  7. Movement Index - EEG evidence of cerebral dysfunction

    Time frame: 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).

Other outcomes

  1. AI Anatomic Landmark Tracking - Post-Menstrual Age at Video

    Time frame: At study completion, an average of 1 week.

    Assess performance of AI anatomic landmark tracking (median position error) across post-menstrual ages at the time of video recording.

  2. AI Anatomic Landmark Tracking - Encephalopathy Status

    Time frame: At study completion, an average of 1 week.

    Assess performance of AI anatomic landmark tracking (median position error) across encephalopathy statuses.

  3. AI Anatomic Landmark Tracking - Caregiver-reported Race/Ethnicity

    Time frame: At study completion, an average of 1 week.

    Assess performance of AI anatomic landmark tracking (median position error) across caregiver-reported race/ethnicity.

  4. AI Anatomic Landmark Tracking - Sex

    Time frame: At study completion, an average of 1 week.

    Assess performance of AI anatomic landmark tracking (median position error) and sex.

  5. AI Anatomic Landmark Tracking - Lighting Conditions

    Time frame: At study completion, an average of 1 week.

    Assess performance of AI anatomic landmark tracking (median position error) and lighting conditions (phototherapy, lights on/off, time of day).

  6. Parent and Provider Feedback

    Time frame: At baseline and study completion (Day 1 - Day 7, on average).

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Florian Richter, PhD

CONTACT

[email protected]

773-312-3301

Saum Naderi, MA

CONTACT

[email protected]

714-913-3641

Sponsors and collaborators

Lead sponsor

Artemis AI Labs

Industry

Registry information

Acronym: NeoNOVA

Important dates

Study start
2026
Primary completion
2027
Study completion
2029
First posted
Jun 5, 2026
Registry last updated
Jul 15, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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