In this study, Optical Coherence Tomography (OCT) was used to image the large upper airway in a rabbit model. U-net convolutional neural network (CNN) was used to automate the segmentation of large airway edema and tissue changes. Peak edema volume was reached at 30-minutes post-chlorine gas exposure, then down trended until the 6-hour timepoint. Herein, we show the streamlining of OCT imaging analysis status-post chlorine inhalation injury using CNNs.
In this work, we demonstrate the ability to image and quantify airway changes, we were able to quantify a decrease in airway compliance. The proposed approach will enable further investigations of using OCT assessing pulmonary injury to prevent/treat ARDS using a chlorine inhalation injury model, as well as diagnosing of large airway injury and compliance change due to airway toxic chemical exposure. With enhanced portability over conventional bronchoscopy, we believe our system is capable of field hospital deployment and investigating airway conditions in warfighters. Combining OCT and pressure transducer with bronchoscopy would enhance assessment and treatment of large airway chemical injury.
In this work, we demonstrate the ability to image and quantify airway changes, edema, and epithelial layer separation using OCT and automated tissue boundary identification in the rabbit large airways as early as 30-minutes post-chlorine gas exposure. We propose this novel approach will enable further investigations into using OCT for pre-hospital and point-of-care diagnostics of large airway injury due to airway toxic chemical exposure. With enhanced portability over conventional bronchoscopy, we believe our system is capable of field hospital deployment and investigating airway conditions in warfighters. Combining OCT with bronchoscopy would enhance the assessment and treatment of large airway chemical injury.
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