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

• Published in: Intensive care medicine experimental Vol. 12; no. 1; pp. 60 - 9
• Main Authors: Cruces, Pablo, Retamal, Jaime, Damián, Andrés, Lago, Graciela, Blasina, Fernanda, Oviedo, Vanessa, Medina, Tania, Pérez, Agustín, Vaamonde, Lucía, Dapueto, Rosina, González-Dambrauskas, Sebastian, Serra, Alberto, Monteverde-Fernandez, Nicolas, Namías, Mauro, Martínez, Javier, Hurtado, Daniel E.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 02.07.2024; Springer Nature B.V; SpringerOpen
• Subjects: Biomechanics; Computed tomography; Critical Care Medicine; Diagnostic imaging; Intensive; Intensive care; Lung strain; Mechanical ventilation; Medicine; Medicine & Public Health; Ventilator-induced lung injury; Ventilators; Lung strain; Diagnostic imaging; Ventilator-induced lung injury; Computed tomography; Mechanical ventilation
• ISSN: 2197-425X; 2197-425X
• DOI: 10.1186/s40635-024-00641-8

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 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. 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 different spatiotemporal regional biomechanical behavior.