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

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dc.contributor.authorCruces, Pablo
dc.contributor.authorRetamal, Jaime
dc.contributor.authorDamián, Andrés
dc.contributor.authorLago, Graciela
dc.contributor.authorBlasina, Fernanda
dc.contributor.authorOviedo, Vanessa
dc.contributor.authorMedina, Tania
dc.contributor.authorPérez, Agustín
dc.contributor.authorVaamonde, Lucía
dc.contributor.authorDapueto, Rosina
dc.contributor.authorGonzález-Dambrauskas, Sebastian
dc.contributor.authorSerra, Alberto
dc.date.accessioned2024-09-09T19:36:45Z
dc.date.available2024-09-09T19:36:45Z
dc.date.issued2024-12
dc.descriptionIndexación: Scopus.
dc.description.abstractBackground: 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.
dc.identifier.citationIntensive Care Medicine Experimental. Volume 12, Issue 1. December 2024 Article number 60
dc.identifier.issn2197-425X
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/59990
dc.language.isoes
dc.publisherSpringer Nature
dc.rights.licenseAtribución/Reconocimiento 4.0 Internacional
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subjectComputed tomography
dc.subjectDiagnostic imaging
dc.subjectLung strain
dc.subjectMechanical ventilation
dc.subjectVentilator-induced lung injury
dc.titleA machine-learning regional clustering approach to understand ventilator-induced lung injury: a proof-of-concept experimental study
dc.typeArtículo
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