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

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

Recruiting
18 years and older
All
Phase N/A
  • Technical (Original)
  • Simplified (AI rewritten)

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Overview

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.

Eligibility

Inclusion Criteria:

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

Exclusion Criteria:

  • current exacerbation

Study details
    Chronic Respiratory Failure

NCT07428694

University of Oslo

14 May 2026

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