Early detection of acute respiratory distress syndrome (ARDS) using optical coherence tomographic (OCT) images by deep learning

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

doi:10.1117/12.2610545

7 March 2022 Early detection of acute respiratory distress syndrome (ARDS) using optical coherence tomographic (OCT) images by deep learning

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

Author Affiliations +

Proceedings Volume 11937, Endoscopic Microscopy XVII; 1193703 (2022) https://doi.org/10.1117/12.2610545
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Acute Respiratory Distress Syndrome (ARDS) is a severe form of lung injury characterized by hypoxemia. ARDS is estimated to affect at least 190,000 patients per year in the United States. The median time for ARDS onset is 48 hours after hospital admission. The early assessment of the ARDS due to smoke inhalation injury (SII) plays a vital role in facilitating appropriate treatment strategies and improved clinical outcomes. Optical coherence tomography (OCT) may be used as an effective diagnostic tool in quantifying the physiological changes in the airway after smoke inhalation injury. The objective of this study is to develop and evaluate a deep-learning technique to predict and early uncover (within 24 hours) ARDS in a pig model based on the information obtained from the OCT images. A convolutional neural network (CNN) is modeled to train and classify the pig airway images. The early prediction would help clinicians in the accurate diagnosis of ARDS which is of great clinical value.

Conference Presentation

Citation Download Citation

Raksha Sreeramachandra Murthy, Yusi Miao, Li-Dek Chou, Andriy I. Batchinsky, and Zhongping Chen "Early detection of acute respiratory distress syndrome (ARDS) using optical coherence tomographic (OCT) images by deep learning", Proc. SPIE 11937, Endoscopic Microscopy XVII, 1193703 (7 March 2022); https://doi.org/10.1117/12.2610545

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