Bibliographic Details
| Title: |
A deep learning model enables accurate prediction and quantification of pulmonary edema from chest X-rays |
| Authors: |
Dominik Schulz, Sebastian Rasch, Markus Heilmaier, Rami Abbassi, Alexander Poszler, Jörg Ulrich, Manuel Steinhardt, Georgios A. Kaissis, Roland M. Schmid, Rickmer Braren, Tobias Lahmer |
| Source: |
Critical Care, Vol 27, Iss 1, Pp 1-4 (2023) |
| Publisher Information: |
BMC, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Medical emergencies. Critical care. Intensive care. First aid |
| Subject Terms: |
Pulmonary edema, Transpulmonary thermodilution, TPTD, Extravascular lung water, EVLWI, Chest X-ray, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9 |
| More Details: |
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 |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1364-8535 |
| Relation: |
https://doaj.org/toc/1364-8535 |
| DOI: |
10.1186/s13054-023-04426-5 |
| Access URL: |
https://doaj.org/article/d25d726d59194ba085e11a819e301134 |
| Accession Number: |
edsdoj.25d726d59194ba085e11a819e301134 |
| Database: |
Directory of Open Access Journals |
