NDLI: A deep learning model enables accurate prediction and quantification of pulmonary edema from chest X-rays

Content Provider	Springer Nature : BioMed Central
Author	Schulz, Dominik Rasch, Sebastian Heilmaier, Markus Abbassi, Rami Poszler, Alexander Ulrich, Jörg Steinhardt, Manuel Kaissis, Georgios A. Schmid, Roland M. Braren, Rickmer Lahmer, Tobias
Abstract	Background A quantitative assessment of pulmonary edema is important because the clinical severity can range from mild impairment to life threatening. A quantitative surrogate measure, although invasive, for pulmonary edema is the extravascular lung water index (EVLWI) extracted from the transpulmonary thermodilution (TPTD). Severity of edema from chest X-rays, to date is based on the subjective classification of radiologists. In this work, we use machine learning to quantitatively predict the severity of pulmonary edema from chest radiography. Methods We retrospectively included 471 X-rays from 431 patients who underwent chest radiography and TPTD measurement within 24 h at our intensive care unit. The EVLWI extracted from the TPTD was used as a quantitative measure for pulmonary edema. We used a deep learning approach and binned the data into two, three, four and five classes increasing the resolution of the EVLWI prediction from the X-rays. Results The accuracy, area under the receiver operating characteristic curve (AUROC) and Mathews correlation coefficient (MCC) in the binary classification models (EVLWI < 15, ≥ 15) were 0.93 (accuracy), 0.98 (AUROC) and 0.86(MCC). In the three multiclass models, the accuracy ranged between 0.90 and 0.95, the AUROC between 0.97 and 0.99 and the MCC between 0.86 and 0.92. Conclusion Deep learning can quantify pulmonary edema as measured by EVLWI with high accuracy.
Related Links	https://ccforum.biomedcentral.com/counter/pdf/10.1186/s13054-023-04426-5.pdf
Ending Page	4
Page Count	4
Starting Page	1
File Format	HTM / HTML
ISSN	13648535
DOI	10.1186/s13054-023-04426-5
Journal	Critical Care
Issue Number	1
Volume Number	27
Language	English
Publisher	BioMed Central
Publisher Date	2023-05-26
Access Restriction	Open
Subject Keyword	Intensive Critical Care Medicine Emergency Medicine Pulmonary edema Transpulmonary thermodilution TPTD Extravascular lung water EVLWI Chest X-ray
Content Type	Text
Resource Type	Article
Subject	Critical Care and Intensive Care Medicine
Journal Impact Factor	8.8/2023
5-Year Journal Impact Factor	10.4/2023

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