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Neonatal Neurological Observation With Video AI (NeoNOVA)

2026년 6월 5일 업데이트: Artemis AI Labs
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).

연구 개요

상태

아직 모집하지 않음

정황

개입 / 치료

상세 설명

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.

연구 유형

관찰

등록 (추정된)

200

연락처 및 위치

이 섹션에서는 연구를 수행하는 사람들의 연락처 정보와 이 연구가 수행되는 장소에 대한 정보를 제공합니다.

연구 연락처

연구 연락처 백업

연구 장소

  • 미국
    • New York
      • New York, New York, 미국, 10029
        • Mount Sinai Hospital
        • 연락하다:
        • 연락하다:
        • 수석 연구원:
          • Benjamin Glicksberg, PhD
      • New York, New York, 미국, 10065
        • Weill Cornell Medicine / NewYork-Presbyterian Hospital
        • 연락하다:
        • 수석 연구원:
          • Sushma Krishna, MD

참여기준

연구원은 적격성 기준이라는 특정 설명에 맞는 사람을 찾습니다. 이러한 기준의 몇 가지 예는 개인의 일반적인 건강 상태 또는 이전 치료입니다.

자격 기준

공부할 수 있는 나이

  • 어린이
  • 성인
  • 고령자

건강한 자원 봉사자를 받아들입니다

예

샘플링 방법

비확률 샘플

연구 인구

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.

설명

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

공부 계획

이 섹션에서는 연구 설계 방법과 연구가 측정하는 내용을 포함하여 연구 계획에 대한 세부 정보를 제공합니다.

연구는 어떻게 설계됩니까?

디자인 세부사항

코호트 및 개입

그룹/코호트
개입 / 치료
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.
장치: Continuous bedside video monitoring with AI anatomic landmark tracking for neurologic monitoring
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.

연구는 무엇을 측정합니까?

주요 결과 측정

결과 측정
측정값 설명
기간
AI Anatomic Landmark Tracking Accuracy
기간: 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.
At study completion, an average of 1 week.

2차 결과 측정

결과 측정
측정값 설명
기간
Movement Index - Encephalopathy measured by modified Sarnat exam
기간: 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.
Through study completion, an average of 1 week.
Movement Index - N-PASS
기간: 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.
Through study completion, an average of 1 week.
Movement Index - Sedative Exposure
기간: Through study completion, an average of 1 week.
Association between movement index and sedative exposure, a routinely collected clinical variable that influences neonatal arousal.
Through study completion, an average of 1 week.
Movement Index - Chronological Age at Video
기간: 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.
Through study completion, an average of 1 week.
Movement Index - Gestational age at birth
기간: 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.
Through study completion, an average of 1 week.
Movement Index - Sleep state
기간: 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.
Through study completion, an average of 1 week.
Movement Index - EEG evidence of cerebral dysfunction
기간: 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).
Through study completion, an average of 1 week.

기타 결과 측정

결과 측정
측정값 설명
기간
AI Anatomic Landmark Tracking - Post-Menstrual Age at Video
기간: 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.
At study completion, an average of 1 week.
AI Anatomic Landmark Tracking - Encephalopathy Status
기간: At study completion, an average of 1 week.
Assess performance of AI anatomic landmark tracking (median position error) across encephalopathy statuses.
At study completion, an average of 1 week.
AI Anatomic Landmark Tracking - Caregiver-reported Race/Ethnicity
기간: At study completion, an average of 1 week.
Assess performance of AI anatomic landmark tracking (median position error) across caregiver-reported race/ethnicity.
At study completion, an average of 1 week.
AI Anatomic Landmark Tracking - Sex
기간: At study completion, an average of 1 week.
Assess performance of AI anatomic landmark tracking (median position error) and sex.
At study completion, an average of 1 week.
AI Anatomic Landmark Tracking - Lighting Conditions
기간: 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).
At study completion, an average of 1 week.
Parent and Provider Feedback
기간: 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.
At baseline and study completion (Day 1 - Day 7, on average).

공동 작업자 및 조사자

여기에서 이 연구와 관련된 사람과 조직을 찾을 수 있습니다.

스폰서

Artemis AI Labs

수사관

  • 수석 연구원: Benjamin Glicksberg, PhD, Icahn School of Medicine at Mount Sinai

간행물 및 유용한 링크

연구에 대한 정보 입력을 담당하는 사람이 자발적으로 이러한 간행물을 제공합니다. 이것은 연구와 관련된 모든 것에 관한 것일 수 있습니다.

일반 간행물

연구 기록 날짜

이 날짜는 ClinicalTrials.gov에 대한 연구 기록 및 요약 결과 제출의 진행 상황을 추적합니다. 연구 기록 및 보고된 결과는 공개 웹사이트에 게시되기 전에 특정 품질 관리 기준을 충족하는지 확인하기 위해 국립 의학 도서관(NLM)에서 검토합니다.

연구 주요 날짜

연구 시작 (추정된)

2026년 6월 1일

기본 완료 (추정된)

2027년 5월 31일

연구 완료 (추정된)

2029년 5월 31일

연구 등록 날짜

최초 제출

2026년 5월 26일

QC 기준을 충족하는 최초 제출

2026년 6월 1일

처음 게시됨 (실제)

2026년 6월 5일

연구 기록 업데이트

마지막 업데이트 게시됨 (실제)

2026년 6월 8일

QC 기준을 충족하는 마지막 업데이트 제출

2026년 6월 5일

마지막으로 확인됨

2026년 6월 1일

추가 정보

이 연구와 관련된 용어

키워드

추가 관련 MeSH 약관

기타 연구 ID 번호

  • STUDY-25-01036

개별 참가자 데이터(IPD) 계획

개별 참가자 데이터(IPD)를 공유할 계획입니까?

아니요

IPD 계획 설명

Aggregate deidentified data and results will be shared. Individual participant video data will not be shared due to PHI concerns.

약물 및 장치 정보, 연구 문서

미국 FDA 규제 의약품 연구

아니

미국 FDA 규제 기기 제품 연구

예

미국에서 제조되어 미국에서 수출되는 제품

아니

이 정보는 변경 없이 clinicaltrials.gov 웹사이트에서 직접 가져온 것입니다. 귀하의 연구 세부 정보를 변경, 제거 또는 업데이트하도록 요청하는 경우 register@clinicaltrials.gov. 문의하십시오. 변경 사항이 clinicaltrials.gov에 구현되는 즉시 저희 웹사이트에도 자동으로 업데이트됩니다. .

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위치

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