- ICH GCP
- Yhdysvaltain kliinisten tutkimusten rekisteri
- Kliininen tutkimus NCT07628829
Neonatal Neurological Observation With Video AI (NeoNOVA)
Tutkimuksen yleiskatsaus
Tila
Yksityiskohtainen kuvaus
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.
Opintotyyppi
Ilmoittautuminen (Arvioitu)
Yhteystiedot ja paikat
Opiskeluyhteys
- Nimi: Saum Naderi, MA
- Puhelinnumero: 714-913-3641
- Sähköposti: saum@artemisailabs.com
Tutki yhteystietojen varmuuskopiointi
- Nimi: Florian Richter, PhD
- Puhelinnumero: 773-312-3301
- Sähköposti: florian@artemisailabs.com
Opiskelupaikat
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New York
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New York, New York, Yhdysvallat, 10029
- Rekrytointi
- Mount Sinai Hospital
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Ottaa yhteyttä:
- Rachelle Weisman, MPH
- Sähköposti: rachelle.weisman@mssm.edu
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Ottaa yhteyttä:
- Klaren Ng
- Puhelinnumero: 347-525-8336
- Sähköposti: klaren.ng@mssm.edu
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Päätutkija:
- Benjamin Glicksberg, PhD
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New York, New York, Yhdysvallat, 10065
- Ei vielä rekrytointia
- Weill Cornell Medicine / NewYork-Presbyterian Hospital
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Ottaa yhteyttä:
- Martha Liu
- Puhelinnumero: 646-697-6428
- Sähköposti: mal4038@med.cornell.edu
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Päätutkija:
- Sushma Krishna, MD
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Osallistumiskriteerit
Kelpoisuusvaatimukset
Opintokelpoiset iät
- Lapsi
- Aikuinen
- Vanhempi Aikuinen
Hyväksyy terveitä vapaaehtoisia
Näytteenottomenetelmä
Tutkimusväestö
Kuvaus
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
Opintosuunnitelma
Miten tutkimus on suunniteltu?
Suunnittelun yksityiskohdat
Kohortit ja interventiot
Ryhmä/Kohortti |
Interventio / Hoito |
|---|---|
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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.
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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.
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Mitä tutkimuksessa mitataan?
Ensisijaiset tulostoimenpiteet
Tulosmittaus |
Toimenpiteen kuvaus |
Aikaikkuna |
|---|---|---|
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AI Anatomic Landmark Tracking Accuracy
Aikaikkuna: At study completion, an average of 1 week.
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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.
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At study completion, an average of 1 week.
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Toissijaiset tulostoimenpiteet
Tulosmittaus |
Toimenpiteen kuvaus |
Aikaikkuna |
|---|---|---|
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Movement Index - Encephalopathy measured by modified Sarnat exam
Aikaikkuna: Through study completion, an average of 1 week.
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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.
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Through study completion, an average of 1 week.
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Movement Index - N-PASS
Aikaikkuna: Through study completion, an average of 1 week.
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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.
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Through study completion, an average of 1 week.
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Movement Index - Sedative Exposure
Aikaikkuna: Through study completion, an average of 1 week.
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Association between movement index and sedative exposure, a routinely collected clinical variable that influences neonatal arousal.
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Through study completion, an average of 1 week.
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Movement Index - Chronological Age at Video
Aikaikkuna: Through study completion, an average of 1 week.
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Association between movement index and chronological age at video, a routinely collected clinical variable that influences neonatal arousal.
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Through study completion, an average of 1 week.
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Movement Index - Gestational age at birth
Aikaikkuna: Through study completion, an average of 1 week.
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Association between the movement index and gestational age at birth, a routinely collected clinical variable that influences neonatal arousal.
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Through study completion, an average of 1 week.
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Movement Index - Sleep state
Aikaikkuna: Through study completion, an average of 1 week.
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Association between the movement index and sleep state, a routinely collected clinical variable that influences neonatal arousal.
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Through study completion, an average of 1 week.
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Movement Index - EEG evidence of cerebral dysfunction
Aikaikkuna: Through study completion, an average of 1 week.
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Association between the movement index and, if obtained as part of routine clinical care, EEG evidence of cerebral dysfunction (a biomarker of encephalopathy).
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Through study completion, an average of 1 week.
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Muut tulostoimenpiteet
Tulosmittaus |
Toimenpiteen kuvaus |
Aikaikkuna |
|---|---|---|
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AI Anatomic Landmark Tracking - Post-Menstrual Age at Video
Aikaikkuna: At study completion, an average of 1 week.
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Assess performance of AI anatomic landmark tracking (median position error) across post-menstrual ages at the time of video recording.
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At study completion, an average of 1 week.
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AI Anatomic Landmark Tracking - Encephalopathy Status
Aikaikkuna: At study completion, an average of 1 week.
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Assess performance of AI anatomic landmark tracking (median position error) across encephalopathy statuses.
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At study completion, an average of 1 week.
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AI Anatomic Landmark Tracking - Caregiver-reported Race/Ethnicity
Aikaikkuna: At study completion, an average of 1 week.
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Assess performance of AI anatomic landmark tracking (median position error) across caregiver-reported race/ethnicity.
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At study completion, an average of 1 week.
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AI Anatomic Landmark Tracking - Sex
Aikaikkuna: At study completion, an average of 1 week.
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Assess performance of AI anatomic landmark tracking (median position error) and sex.
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At study completion, an average of 1 week.
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AI Anatomic Landmark Tracking - Lighting Conditions
Aikaikkuna: At study completion, an average of 1 week.
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Assess performance of AI anatomic landmark tracking (median position error) and lighting conditions (phototherapy, lights on/off, time of day).
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At study completion, an average of 1 week.
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Parent and Provider Feedback
Aikaikkuna: At baseline and study completion (Day 1 - Day 7, on average).
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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.
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At baseline and study completion (Day 1 - Day 7, on average).
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Yhteistyökumppanit ja tutkijat
Sponsori
Tutkijat
- Päätutkija: Benjamin Glicksberg, PhD, Icahn School of Medicine at Mount Sinai
Julkaisuja ja hyödyllisiä linkkejä
Yleiset julkaisut
- 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.
- 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.
Opintojen ennätyspäivät
Opi tärkeimmät päivämäärät
Opiskelun aloitus (Todellinen)
Ensisijainen valmistuminen (Arvioitu)
Opintojen valmistuminen (Arvioitu)
Opintoihin ilmoittautumispäivät
Ensimmäinen lähetetty
Ensimmäinen toimitettu, joka täytti QC-kriteerit
Ensimmäinen Lähetetty (Todellinen)
Tutkimustietojen päivitykset
Viimeisin päivitys julkaistu (Todellinen)
Viimeisin lähetetty päivitys, joka täytti QC-kriteerit
Viimeksi vahvistettu
Lisää tietoa
Tähän tutkimukseen liittyvät termit
Avainsanat
Muita asiaankuuluvia MeSH-ehtoja
Muut tutkimustunnusnumerot
- STUDY-25-01036
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Aiotko jakaa yksittäisten osallistujien tietoja (IPD)?
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Tutkii yhdysvaltalaista FDA sääntelemää laitetuotetta
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