A systematic review of machine learning models for management, prediction and classification of ARDS.

Aim: Acute respiratory distress syndrome or ARDS is an acute, severe form of respiratory failure characterised by poor oxygenation and bilateral pulmonary infiltrates. Advancements in signal processing and machine learning have led to promising solutions for classification, event detection and predictive models in the management of ARDS.

Method: In this review, we provide systematic description of different studies in the application of Machine Learning (ML) and artificial intelligence for management, prediction, and classification of ARDS.

Quantifying heterogeneity in an animal model of acute respiratory distress syndrome, a comparison of inspired sinewave technique to computed tomography.

The inspired sinewave technique (IST) is a non-invasive method to measure lung heterogeneity indices (including both uneven ventilation and perfusion or heterogeneity), which reveal multiple conditions of the lung and lung injury. To evaluate the reproducibility and predicted clinical outcomes of IST heterogeneity values, a comparison with a quantitative lung computed tomography (CT) scan is performed. Six anaesthetised pigs were studied after surfactant depletion by saline-lavage.

Assessing quality of life for multidrug-resistant and extensively drug-resistant tuberculosis patients.

Objective: One can hypothesize that Mycobacterium genus originated more than 150 million years ago and has evolved to become one of the leading lethal infectious diseases. Multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) patients are directly affected by the disease and other subjective issues, such as related diseases, medical costs and social issues, which all have negative impacts on patient quality of life (QOL). Our purpose is to define the status of health-related QOL for international MDR-TB and XDR-TB patients.