Microscopic computed tomography with AI-CNN-powered image analysis: the path to phenotype bleomycin-induced lung injury.

Autor: Henneke I; Experimental Lung Disease Models Platform, Institute for Lung Health (ILH), Justus Liebig University (JLU), Giessen, Germany.; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany., Pilz C; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany., Wilhelm J; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany.; Genomics and Bioinformatics Platform, Institute for Lung Health (ILH), Justus Liebig University (JLU), Giessen, Germany., Alexopoulos I; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany.; Multyscale Imaging Platform, Institute for Lung Health (ILH), Justus Liebig University (JLU), Giessen, Germany., Ezaddoustdar A; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany.; Center for Infection and Genomics of the Lung (CIGL), Faculty of Medicine, Justus Liebig University (JLU), Giessen, Germany., Mukhametshina R; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany.; Small Animal Imaging Platform, Institute for Lung Health (ILH), Justus Liebig University (JLU), Giessen, Germany., Weissmann N; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany., Ghofrani HA; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany., Grimminger F; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany.; Institute for Lung Health (ILH), Justus Liebig University (JLU), Giessen, Germany., Seeger W; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany.; Institute for Lung Health (ILH), Justus Liebig University (JLU), Giessen, Germany.; Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany., Schermuly RT; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany., Wygrecka M; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany.; Center for Infection and Genomics of the Lung (CIGL), Faculty of Medicine, Justus Liebig University (JLU), Giessen, Germany.; CSL Behring Innovation GmbH, Marburg, Germany., Kojonazarov B; Department of Internal Medicine, Member of the German Center for Lung Research (DZL), Member of the Excellence Cluster Cardio-Pulmonary Institute (CPI), Universities of Giessen and Murburg Lung Center (UGMLC), Justus Liebig University (JLU), Giessen, Germany.; Small Animal Imaging Platform, Institute for Lung Health (ILH), Justus Liebig University (JLU), Giessen, Germany.
Jazyk: angličtina
Zdroj: American journal of physiology. Cell physiology [Am J Physiol Cell Physiol] 2024 Jun 01; Vol. 326 (6), pp. C1637-C1647. Date of Electronic Publication: 2024 Apr 22.
DOI: 10.1152/ajpcell.00708.2023
Abstrakt: Bleomycin (BLM)-induced lung injury in mice is a valuable model for investigating the molecular mechanisms that drive inflammation and fibrosis and for evaluating potential therapeutic approaches to treat the disease. Given high variability in the BLM model, it is critical to accurately phenotype the animals in the course of an experiment. In the present study, we aimed to demonstrate the utility of microscopic computed tomography (µCT) imaging combined with an artificial intelligence (AI)-convolutional neural network (CNN)-powered lung segmentation for rapid phenotyping of BLM mice. µCT was performed in freely breathing C57BL/6J mice under isoflurane anesthesia on days 7 and 21 after BLM administration. Terminal invasive lung function measurement and histological assessment of the left lung collagen content were conducted as well. µCT image analysis demonstrated gradual and time-dependent development of lung injury as evident by alterations in the lung density, air-to-tissue volume ratio, and lung aeration in mice treated with BLM. The right and left lung were unequally affected. µCT-derived parameters such as lung density, air-to-tissue volume ratio, and nonaerated lung volume correlated well with the invasive lung function measurement and left lung collagen content. Our study demonstrates the utility of AI-CNN-powered µCT image analysis for rapid and accurate phenotyping of BLM mice in the course of disease development and progression. NEW & NOTEWORTHY Microscopic computed tomography (µCT) imaging combined with an artificial intelligence (AI)-convolutional neural network (CNN)-powered lung segmentation is a rapid and powerful tool for noninvasive phenotyping of bleomycin mice over the course of the disease. This, in turn, allows earlier and more reliable identification of therapeutic effects of new drug candidates, ultimately leading to the reduction of unnecessary procedures in animals in pharmacological research.
Databáze: MEDLINE
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