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

University of Oslo20 enrolled

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.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Chronic Respiratory Failure

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * elective hospitalisation for control of non-invasive ventilation * use of ResMedLumis 100/150 ventilator * treatment for \>3 months Exclusion Criteria: * current exacerbation

Locations (1)

Oslo University Hospital

Oslo, Norway

RECRUITING

Outcomes

Primary Outcomes

Correct interpretation of inspiratory leak by machine learning tool

Measured in comparison with god standard method of polygraphy

Time frame: one year

Data from ClinicalTrials.gov

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