Neonatal Neurological Observation With Video AI (NeoNOVA)
研究概览
地位
详细说明
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.
研究类型
注册 (估计的)
联系人和位置
学习联系方式
- 姓名:Saum Naderi, MA
- 电话号码:714-913-3641
- 邮箱:saum@artemisailabs.com
研究联系人备份
- 姓名:Florian Richter, PhD
- 电话号码:773-312-3301
- 邮箱:florian@artemisailabs.com
学习地点
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New York
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New York、New York、美国、10029
- 招聘中
- Mount Sinai Hospital
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接触:
- Rachelle Weisman, MPH
- 邮箱:rachelle.weisman@mssm.edu
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接触:
- Klaren Ng
- 电话号码:347-525-8336
- 邮箱:klaren.ng@mssm.edu
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首席研究员:
- Benjamin Glicksberg, PhD
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New York、New York、美国、10065
- 尚未招聘
- Weill Cornell Medicine / NewYork-Presbyterian Hospital
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接触:
- Martha Liu
- 电话号码:646-697-6428
- 邮箱:mal4038@med.cornell.edu
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首席研究员:
- Sushma Krishna, MD
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参与标准
资格标准
适合学习的年龄
- 孩子
- 成人
- 年长者
接受健康志愿者
取样方法
研究人群
描述
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
学习计划
研究是如何设计的?
设计细节
队列和干预
团体/队列 |
干预/治疗 |
|---|---|
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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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研究衡量的是什么?
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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AI Anatomic Landmark Tracking Accuracy
大体时间: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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次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Movement Index - Encephalopathy measured by modified Sarnat exam
大体时间: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
大体时间: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
大体时间: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
大体时间: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
大体时间: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
大体时间: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
大体时间: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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其他结果措施
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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AI Anatomic Landmark Tracking - Post-Menstrual Age at Video
大体时间: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
大体时间: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
大体时间: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
大体时间: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
大体时间: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
大体时间: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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合作者和调查者
调查人员
- 首席研究员:Benjamin Glicksberg, PhD、Icahn School of Medicine at Mount Sinai
出版物和有用的链接
一般刊物
- 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.
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (估计的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
关键字
其他研究编号
- STUDY-25-01036
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IPD 计划说明
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