From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak
Recruiting
Observational Study
Chronic Respiratory Failure
No Study Drug
Researchers observe your health over time — no experimental treatment is given.
At a Glance
- Age
- 18 and older
- Sex
- Any
- Study type
- Observational
- Participants needed
- 20 (estimated)
- Sponsor
- University of Oslo · Other
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About This Trial
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.
Trial Locations
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Eligibility Criteria
Inclusion Criteria:
* elective hospitalisation for control of non-invasive ventilation
* use of ResMedLumis 100/150 ventilator
* treatment for \>3 months
Exclusion Criteria:
* current exacerbation
Contacts
No contact information available.