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NCT Number: NCT07428694

From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak

Study of the applicability of machine learning tools in detecting inspiratory leakage in longterm non-invasive ventilation. The study was conducted in two stages. Firstly the ML model was trained on both bench model created scenarios and then ten patients. And secondly the success of the model was assessed in a proof of concept pilot study of ten patients.

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Key information

Conditions

Chronic Respiratory Failure

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • elective hospitalisation for control of non-invasive ventilation
  • use of ResMedLumis 100/150 ventilator
  • treatment for >3 months

Exclusion criteria

  • current exacerbation

Treatment and study plan

Primary outcomes

  1. Correct interpretation of inspiratory leak by machine learning tool

    Time frame: one year

    Measured in comparison with god standard method of polygraphy

Sponsors and collaborators

Lead sponsor

University of Oslo

Other

Collaborators

  • Oslo University Hospital

Registry information

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Feb 24, 2026
Registry last updated
Feb 24, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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