Neonatal EncephalopathyNeoNOVANCT07628829
Neonatal Neurological Observation With Video AI
An observational study of Neonatal Encephalopathy and Hypoxic-Ischemic Encephalopathy, run by Artemis AI Labs at 2 sites in United States.
Trial 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… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Neonatal Encephalopathy, Hypoxic-Ischemic Encephalopathy, Sedation
- What is being tested?
- Continuous bedside video monitoring with AI anatomic landmark tracking for neurologic monitoring
- Type of treatment
- Medical device
- How many people
- 200 participants
- Where
- 2 sitesUnited States
- Study dates
- 13 Jul 2026to 12 Jul 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- Parents or LAR unable to provide informed consent or are under the age of 18.
- Non-viable neonates
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
United States2 sites, 2 recruiting
- Recruiting
Mount Sinai Hospital
New York, New York, 10029
Klaren Ng, 347-525-8336, klaren.ng@mssm.edu
Rachelle Weisman, MPH, rachelle.weisman@mssm.edu
- Not yet recruiting
Weill Cornell Medicine / NewYork-Presbyterian Hospital
New York, New York, 10065
Martha Liu, 646-697-6428, mal4038@med.cornell.edu
Sushma Krishna, MD
About this study
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).
Read the 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.
- Being tested
- Continuous bedside video monitoring with AI anatomic landmark tracking for neurologic monitoring
- 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
- Medical subject headings
- Hypoxia-Ischemia, Brain, Brain Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Continuous bedside video monitoring with AI anatomic landmark tracking for neurologic monitoringDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: Continuous bedside video monitoring with AI anatomic landmark tracking for neurologic monitoring |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: At study completion, an average of 1 week.
Secondary outcomes (7)
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.
Measured: Through study completion, an average of 1 week.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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).
Measured: Through study completion, an average of 1 week.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jun 2026
- Study start13 Jul 2026
- Primary completion12 Jul 2027 (estimated)
- Study completion12 Jul 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Neonatal EncephalopathyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Artemis AI Labs
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Benjamin Glicksberg, PhD, Principal investigator, Icahn School of Medicine at Mount Sinai
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07628829
- Sponsor's protocol ID
- STUDY-25-01036
- Acronym
- NeoNOVA
- Status last verified
- Jul 2026
- First submitted
- 26 May 2026
- Last updated
- 15 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No: Aggregate deidentified data and results will be shared. Individual participant video data will not be shared due to PHI concerns.
Publications
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.
PubMed 41532764Gleason 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.
PubMed 39764545
Study at a glance
From the registry record, last updated by the sponsor on 15 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Neonatal Neurological Observation With Video AI
- ClinicalTrials.gov ID
- NCT07628829
- Acronym / sponsor ID
- NeoNOVA / STUDY-25-01036
- Keywords
- Pose AI, Neonatal, Neonate, AI, Video AI, artificial intelligence, computer vision, NICU
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Artemis AI LabsIndustry (pharma, biotech, devices)
- Study start
- 13 Jul 2026 (actual)
- Primary completion
- 12 Jul 2027 (estimated)
- Study completion
- 12 Jul 2029 (estimated)
- First posted
- 5 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| NICU-Admitted Infants Undergoing Continuous Video Monitoring | – | Device: Continuous bedside video monitoring with AI anatomic landmark tracking for neurologic monitoringInfants 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- Parents or LAR unable to provide informed consent or are under the age of 18.
- Non-viable neonates
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Continuous bedside video monitoring with AI anatomic landmark tracking for neurologic monitoringA 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. | Device | NICU-Admitted Infants Undergoing Continuous Video Monitoring |
Primary outcome
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.
7 secondary outcomes
Movement Index - Encephalopathy measured by modified Sarnat exam
Time frame: Through study completion, an average of 1 week.
Movement Index - N-PASS
Time frame: Through study completion, an average of 1 week.
Movement Index - Sedative Exposure
Time frame: Through study completion, an average of 1 week.
Movement Index - Chronological Age at Video
Time frame: Through study completion, an average of 1 week.
Movement Index - Gestational age at birth
Time frame: Through study completion, an average of 1 week.
Movement Index - Sleep state
Time frame: Through study completion, an average of 1 week.
Movement Index - EEG evidence of cerebral dysfunction
Time frame: Through study completion, an average of 1 week.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jul 2026.
- Background: PubMed 41532764Feng 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.
- Background: PubMed 39764545Gleason 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.
- Individual participant dataSharing planned: No. Aggregate deidentified data and results will be shared. Individual participant video data will not be shared due to PHI concerns.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Benjamin Glicksberg, PhD principal investigator
Icahn School of Medicine at Mount Sinai
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Neonatal Encephalopathy and Hypoxic-Ischemic Encephalopathy, run by Artemis AI Labs at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 15 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Saum Naderi, MA (714-913-3641, saum@artemisailabs.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AI Anatomic Landmark Tracking Accuracy (time frame: At study completion, an average of 1 week.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07628829, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Jul 2026.