From Bench to Bedside: A Machine Learning Tool for the Detection of Inspiratory Leak
ConditionChronic Respiratory Failure
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorUniversity 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.
elective hospitalisation for control of non-invasive ventilation
use of ResMedLumis 100/150 ventilator
treatment for >3 months
current exacerbation
University of Oslo
Lead sponsor
Oslo University Hospital
Collaborator