{Reference Type}: Journal Article
{Title}: A machine-learning regional clustering approach to understand ventilator-induced lung injury: a proof-of-concept experimental study.
{Author}: Cruces P;Retamal J;Damián A;Lago G;Blasina F;Oviedo V;Medina T;Pérez A;Vaamonde L;Dapueto R;González-Dambrauskas S;Serra A;Monteverde-Fernandez N;Namías M;Martínez J;Hurtado DE;
{Journal}: Intensive Care Med Exp
{Volume}: 12
{Issue}: 1
{Year}: 2024 Jul 2
暂无{DOI}: 10.1186/s40635-024-00641-8
{Abstract}: <B>BACKGROUND: </B>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.<BR><B>RESULTS: </B>Ten anesthetized pigs (27 ± 2 kg) were studied. Eight subjects were analyzed. End-inspiratory and end-expiratory 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 identified three different clusters of lung tissue: Stable, Recruitable Unstable, and Non-Recruitable Unstable. End-expiratory aeration, tidal recruitment, and volumetric strain were significantly different 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.<BR><B>CONCLUSIONS: </B>VILI is a regional and dynamic phenomenon. Using unbiased machine-learning techniques we can identify the coexistence of three functional lung tissue compartments with different spatiotemporal regional biomechanical behavior.