1. A Web-Based Platform for the Automatic Stratification of ARDS Severity.
- Author
-
Yahyatabar, Mohammad, Jouvet, Philippe, Fily, Donatien, Rambaud, Jérome, Levy, Michaël, Khemani, Robinder G., and Cheriet, Farida
- Subjects
- *
ADULT respiratory distress syndrome - Abstract
Acute respiratory distress syndrome (ARDS), including severe pulmonary COVID infection, is associated with a high mortality rate. It is crucial to detect ARDS early, as a late diagnosis may lead to serious complications in treatment. One of the challenges in ARDS diagnosis is chest X-ray (CXR) interpretation. ARDS causes diffuse infiltrates through the lungs that must be identified using chest radiography. In this paper, we present a web-based platform leveraging artificial intelligence (AI) to automatically assess pediatric ARDS (PARDS) using CXR images. Our system computes a severity score to identify and grade ARDS in CXR images. Moreover, the platform provides an image highlighting the lung fields, which can be utilized for prospective AI-based systems. A deep learning (DL) approach is employed to analyze the input data. A novel DL model, named Dense-Ynet, is trained using a CXR dataset in which clinical specialists previously labelled the two halves (upper and lower) of each lung. The assessment results show that our platform achieves a recall rate of 95.25 % and a precision of 88.02 % . The web platform, named PARDS-CxR, assigns severity scores to input CXR images that are compatible with current definitions of ARDS and PARDS. Once it has undergone external validation, PARDS-CxR will serve as an essential component in a clinical AI framework for diagnosing ARDS. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF