- ICH GCP
- Registro de ensayos clínicos de EE. UU.
- Ensayo clínico NCT07628829
Neonatal Neurological Observation With Video AI (NeoNOVA)
Descripción general del estudio
Estado
Descripción detallada
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.
Tipo de estudio
Inscripción (Estimado)
Contactos y Ubicaciones
Estudio Contacto
- Nombre: Saum Naderi, MA
- Número de teléfono: 714-913-3641
- Correo electrónico: saum@artemisailabs.com
Copia de seguridad de contactos de estudio
- Nombre: Florian Richter, PhD
- Número de teléfono: 773-312-3301
- Correo electrónico: florian@artemisailabs.com
Ubicaciones de estudio
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New York
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New York, New York, Estados Unidos, 10029
- Reclutamiento
- Mount Sinai Hospital
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Contacto:
- Rachelle Weisman, MPH
- Correo electrónico: rachelle.weisman@mssm.edu
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Contacto:
- Klaren Ng
- Número de teléfono: 347-525-8336
- Correo electrónico: klaren.ng@mssm.edu
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Investigador principal:
- Benjamin Glicksberg, PhD
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New York, New York, Estados Unidos, 10065
- Aún no reclutando
- Weill Cornell Medicine / NewYork-Presbyterian Hospital
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Contacto:
- Martha Liu
- Número de teléfono: 646-697-6428
- Correo electrónico: mal4038@med.cornell.edu
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Investigador principal:
- Sushma Krishna, MD
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
- Niño
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
Método de muestreo
Población de estudio
Descripción
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
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
Cohortes e Intervenciones
Grupo / Cohorte |
Intervención / Tratamiento |
|---|---|
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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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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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AI Anatomic Landmark Tracking Accuracy
Periodo de tiempo: 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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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Movement Index - Encephalopathy measured by modified Sarnat exam
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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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Otras medidas de resultado
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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AI Anatomic Landmark Tracking - Post-Menstrual Age at Video
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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
Periodo de tiempo: 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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Colaboradores e Investigadores
Patrocinador
Investigadores
- Investigador principal: Benjamin Glicksberg, PhD, Icahn School of Medicine at Mount Sinai
Publicaciones y enlaces útiles
Publicaciones Generales
- 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.
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Actual)
Finalización primaria (Estimado)
Finalización del estudio (Estimado)
Fechas de registro del estudio
Enviado por primera vez
Primero enviado que cumplió con los criterios de control de calidad
Publicado por primera vez (Actual)
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
Última actualización enviada que cumplió con los criterios de control de calidad
Última verificación
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
- Trastornos cerebrovasculares
- Enfermedades del Sistema Nervioso Central
- Enfermedades del Sistema Nervioso
- Enfermedades Vasculares
- Enfermedades cardiovasculares
- Isquemia cerebral
- Signos y Síntomas Respiratorios
- Hipoxia, Cerebro
- Hipoxia
- Condiciones Patológicas, Signos y Síntomas
- Signos y síntomas
- Enfermedades Cerebrales
- Hipoxia-Isquemia Cerebral
Otros números de identificación del estudio
- STUDY-25-01036
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
Descripción del plan IPD
Información sobre medicamentos y dispositivos, documentos del estudio
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