Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.

Journal: Journal of critical care

Published Date: Mar 28, 2024

Abstract

OBJECTIVE: This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia, acute respiratory distress syndrome (ARDS) and normal lungs.

Authors

Run Zhou Ye

Division of Endocrinology, Department of Medicine, Centre de recherche du Centre hospitalier universitaire de Sherbrooke, Université de Sherbrooke, Sherbrooke, Quebec, Canada.
Kirill Lipatov

Division of Pulmonary and Critical Care Medicine.
Daniel Diedrich

Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA.
Anirban Bhattacharyya

Critical Care Medicine, Mayo Clinic in Florida, Jacksonville, Florida, USA.
Bradley J Erickson

Department of Radiology, Radiology Informatics Lab, Mayo Clinic, Rochester, MN 55905, United States.
Brian W Pickering

Department of Anesthesiology, Mayo Clinic, Rochester, Minnesota, United States.
Vitaly Herasevich

Department of Anesthesiology, Mayo Clinic, Rochester, MN, USA.

Keywords

Adult Deep Learning Female Humans Intensive Care Units Male Middle Aged Neural Networks, Computer Pneumonia Radiography, Thoracic Respiratory Distress Syndrome Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (38552452)

Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks.

Abstract

Authors

Keywords

External Resources

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