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

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Conditions studied: Chronic Respiratory Failure

In brief

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.

Key facts

Study ID

NCT07428694

Run by

University of Oslo

People needed

20

Starts

2025-10-01

Expected to finish

2026-10-01

Last updated by the study team

2026-02-24

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

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

You may not qualify if…

• current exacerbation

Where it is running

• Oslo University Hospital — Oslo, Norway (enrolling)

Full record on ClinicalTrials.gov

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