Deep learning-based assessment of acute respiratory distress syndrome (ARDS) using optical coherence tomography (OCT)

Raksha Sreeramachandra Murthy; Yusi Miao; Li-Dek Chou; Andriy I. Batchinsky; Zhongping Chen

doi:10.1117/12.2670002

17 March 2023 Deep learning-based assessment of acute respiratory distress syndrome (ARDS) using optical coherence tomography (OCT)

Raksha Sreeramachandra Murthy, Yusi Miao, Li-Dek Chou, Andriy I. Batchinsky, Zhongping Chen

Author Affiliations +

Proceedings Volume 12354, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2023; 123540K (2023) https://doi.org/10.1117/12.2670002
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Acute Respiratory Distress Syndrome (ARDS) is a heterogenic clinical condition that affects critically-ill patients and is associated with high mortality rates and treatment costs. It is characterized by severe acute hypoxemia and alveolar lung injuries. We previously designed an optical coherence tomography (OCT) system to evaluate the changes in mucosa thickness (MT) and proximal airway volume in a swine model after a smoke inhalation injury. However, the analysis relied on manual segmentation of OCT images. Since the manual segmentation of large amounts of OCT data is time-consuming, tedious, and prone to error, this study aims to assess proximal airway volume (PAV) using an automated method based on deep learning. We use convolutional neural networks (CNN) to calculate PAV in a swine model affected by ARDS. We compare the PAV of the swine affected by ARDS with non-ARDS swine. We evaluate OCT images obtained at baseline (BL), post-injury (PI), 24 hours, 48 hours, and 72 hours after smoke inhalation injury. The neural network is modeled utilizing the U-net architecture. The accuracy is evaluated by computing the Sørensen-Dice similarity coefficient. We also demonstrate the correlation between PAV and MT, PFR values obtained from our previous study.

Conference Presentation

Citation Download Citation

Raksha Sreeramachandra Murthy, Yusi Miao, Li-Dek Chou, Andriy I. Batchinsky, and Zhongping Chen "Deep learning-based assessment of acute respiratory distress syndrome (ARDS) using optical coherence tomography (OCT)", Proc. SPIE 12354, Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2023, 123540K (17 March 2023); https://doi.org/10.1117/12.2670002

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