Assessment of proximal airway volume (PAV) in acute respiratory distress syndrome (ARDS) using optical coherence tomography (OCT) through deep learning

Raksha Sreeramachandra Murthy; Li-Dek Chou; Andriy Batchinsky; Zhongping Chen

doi:10.1117/12.3003599

13 March 2024 Assessment of proximal airway volume (PAV) in acute respiratory distress syndrome (ARDS) using optical coherence tomography (OCT) through deep learning

Raksha Sreeramachandra Murthy, Li-Dek Chou, Andriy Batchinsky, Zhongping Chen

Author Affiliations +

Proceedings Volume PC12818, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2024; (2024) https://doi.org/10.1117/12.3003599
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Acute Respiratory Distress Syndrome (ARDS) is a life-threatening condition in critically ill patients, characterized by severe acute hypoxemia and lung injuries. The complexity of ARDS and its high mortality rates warrant innovative approaches for accurate assessment and early intervention. In this scientific study, we present an advanced methodology to evaluate proximal airway volume (PAV) in a porcine model using optical coherence tomography (OCT) and deep learning techniques. We developed an OCT system capable of capturing changes in mucosa thickness (MT) and proximal airway volume in response to smoke inhalation injury in the porcine model. OCT images were acquired at various time points, including baseline, post-injury, and 24, 48, and 72 hours after injury. A comprehensive dataset was compiled for training and validating the deep learning models. The deep learning approach, employing U-Net, DeepLabv3, and SegNet architectures, demonstrated remarkable efficiency in automated PAV calculation when compared to manual segmentation. The Intersection over Union and Dice similarity coefficient metrics validated the accuracy of the models in delineating proximal airway structures.

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Raksha Sreeramachandra Murthy, Li-Dek Chou, Andriy Batchinsky, and Zhongping Chen "Assessment of proximal airway volume (PAV) in acute respiratory distress syndrome (ARDS) using optical coherence tomography (OCT) through deep learning", Proc. SPIE PC12818, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2024, (13 March 2024); https://doi.org/10.1117/12.3003599

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KEYWORDS

Optical coherence tomography

Deep learning

Image segmentation

Injuries

Animal model studies

Animals

Error analysis

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