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NewbornTime – Improved newborn care based on video and artificial intelligence

NewbornTime – Improved newborn care based on video and artificial intelligence

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ISRCTN
Registry ID
ISRCTN12236970
Enrollment
1000
Registered
2023-02-22
Start date
2021-11-15
Completion date
Unknown
Last updated
2025-10-20

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Newborn resuscitation Pregnancy and Childbirth

Interventions

This study investigates newborn resuscitation because birth asphyxia is a primary cause of death in newborns, and immediate resuscitation of the newborn is crucial to reduce the risk. Currently, there
it can detect deviations and it can identify areas where there is a need for better routines or training. Resuscitation activities include stimulation, clearing airways and performing bag-mask ventila

Sponsors

University of Stavanger
Lead Sponsor
Stavanger University Hospital
Collaborator

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. All women giving birth at the hospital 2. All newborns requiring resuscitation

Exclusion criteria

Exclusion criteria: 1. Non-consent from the mother 2. HCPs refraining from participation within 48 hours of the event 3. Birth in the labour room without cameras installed and no newborn resuscitation necessary

Design outcomes

Primary

MeasureTime frame
NewbornTimeline will be evaluated through the following outcome variables: 1. Time of birth measured using the artificial intelligence-based judgement of the video recording of the labor by a thermal camera and manually recorded time as ground truth at the time of birth in the delivery room 2. The resuscitation activities and events measured using the artificial intelligence-based judgement of the visual light video recording from the resuscitation table and manually labeled videos in retrospect as ground truth at the time of resuscitation

Secondary

MeasureTime frame
1. Compliance with resuscitation guidelines measured by comparing the manually labeled timelines and the AI-produced timelines with resuscitation guidelines in retrospect towards the end of the project when the AI models are working and the manual labeling is terminated 2. Successful resuscitation activity patterns measured using machine learning on the series of timelines compared with medical records on heart rate and the condition of the newborn at the end of the resuscitation in retrospect towards the end of the project 3. Number of consents, generated study IDs and collected videos of different types collected as a function of time measured using a digital patient consent handling and automated video data collection system to facilitate secure and accountable data collection throughout the data collection period

Countries

Norway

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 9, 2026