Actively Recruiting
From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak
Led by University of Oslo · Updated on 2026-02-24
20
Participants Needed
1
Research Sites
52 weeks
Total Duration
On this page
Sponsors
U
University of Oslo
Lead Sponsor
O
Oslo University Hospital
Collaborating Sponsor
AI-Summary
What this Trial Is About
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.
CONDITIONS
Official Title
From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Elective hospitalization for control of non-invasive ventilation
- Use of ResMed Lumis 100 or 150 ventilator
- Treatment with non-invasive ventilation for more than 3 months
You will not qualify if you...
- Current exacerbation of respiratory condition
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Oslo University Hospital
Oslo, Norway
Actively Recruiting
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
0
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