Automated detection of acute respiratory distress syndrome from chest X-Rays using Directionality Measure and deep learning features.

ABSTRACT: Acute respiratory distress syndrome (ARDS) is a life-threatening lung injury with global prevalence and high mortality. Chest x-rays (CXR) are critical in the early diagnosis and treatment of ARDS. However, imaging findings may not result in proper identification of ARDS due to a number of reasons, including nonspecific appearance of radiological features, ambiguity in a patient's case due to the pathological stage of the disease, and poor inter-rater reliability from interpretations of CXRs by multiple clinical experts. This study demonstrates the potential capability of methodologies in artificial intelligence, machine learning, and image processing to overcome these challenges and quantitatively assess CXRs for presence of ARDS. We propose and describe Directionality Measure, a novel fe

SUBMITTER: Reamaroon N 

PROVIDER: S-EPMC9169678 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature