A machine-learning regional clustering approach to understand ventilator-induced lung injury: a proof-of-concept experimental study

Abstract
Background The spatiotemporal progression and patterns of tissue deformation in ventilator-induced lung injury (VILI) remain understudied. Our aim was to identify lung clusters based on their regional mechanical behavior over space and time in lungs subjected to VILI using machine-learning techniques. Results Ten anesthetized pigs (27±2 kg) were studied. Eight subjects were analyzed. End-inspiratory and endexpiratory lung computed tomography scans were performed at the beginning and after 12 h of one-hit VILI model. Regional image-based biomechanical analysis was used to determine end-expiratory aeration, tidal recruitment, and volumetric strain for both early and late stages. Clustering analysis was performed using principal component analysis and K-Means algorithms. We identifed three diferent clusters of lung tissue: Stable, Recruitable Unstable, and Non-Recruitable Unstable. End-expiratory aeration, tidal recruitment, and volumetric strain were signifcantly diferent between clusters at early stage. At late stage, we found a step loss of end-expiratory aeration among clusters, lowest in Stable, followed by Unstable Recruitable, and highest in the Unstable Non-Recruitable cluster. Volumetric strain remaining unchanged in the Stable cluster, with slight increases in the Recruitable cluster, and strong reduction in the Unstable Non-Recruitable cluster. Conclusions VILI is a regional and dynamic phenomenon. Using unbiased machine-learning techniques we can identify the coexistence of three functional lung tissue compartments with diferent spatiotemporal regional biomechanical behavior.
Description
Keywords
Mechanical ventilation, Ventilator-induced lung injury, Lung strain, Computed tomography, Diagnostic imaging
Citation
Intensive Care Medicine Experimental. 2024 Jul 02;12(1):60