Oscillometry and Machine Learning Approaches

Overview

Unicentric retrospective study designed to analyses the performance of various machine learning approaches to predict patterns of chronic respiratory diseases such as asthma, based mainly on clinical information and respiratory spirometry/oscillometry.

Impulse oscillometry is a technique that allows evaluation of pulmonary mechanics through the application of sound waves of different frequencies, collecting the oscillations produced in the patient in response. The use of mathematical algorithms in the interpretation of oscillometry improves the evaluation of pulmonary function. The aim of the present study is to evaluate machine learning approaches to recognize respiratory patterns of different diseases.

Conditions

Interventions

Eligibility

Locations (1)

Outcomes

Central Contacts