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

February 17, 2026 updated by: Marte Skogstad Allgot, University of Oslo
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.

Study Overview

Status

Recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

20

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Norway
      • Oslo, Norway
        • Recruiting
        • Oslo University Hospital
        • Contact:
        • Contact:
          • S
          • Phone Number: +4799616202

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients undergoing treatment with Lumis 100/150 for type 2 chronic resiratory failure

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Correct interpretation of inspiratory leak by machine learning tool
Time Frame: one year
Measured in comparison with god standard method of polygraphy
one year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Oslo

Collaborators

Oslo University Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 1, 2025

Primary Completion (Estimated)

October 1, 2026

Study Completion (Estimated)

October 1, 2026

Study Registration Dates

First Submitted

February 17, 2026

First Submitted That Met QC Criteria

February 17, 2026

First Posted (Actual)

February 24, 2026

Study Record Updates

Last Update Posted (Actual)

February 24, 2026

Last Update Submitted That Met QC Criteria

February 17, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

  • 878631

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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