A machine-learning regional clustering approach to understand ventilator-induced lung injury: a proof-of-concept experimental study
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Archivos
Fecha
2024-12
Profesor/a Guía
Facultad/escuela
Idioma
es
Título de la revista
ISSN de la revista
Título del volumen
Editor
Springer Nature
Nombre de Curso
Licencia CC
Atribución/Reconocimiento 4.0 Internacional
Licencia CC
https://creativecommons.org/licenses/by/4.0/deed.es
Resumen
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. © The Author(s) 2024.
Notas
Indexación: Scopus.
Palabras clave
Computed tomography, Diagnostic imaging, Lung strain, Mechanical ventilation, Ventilator-induced lung injury
Citación
Intensive Care Medicine Experimental. Volume 12, Issue 1. December 2024 Article number 60