User: Guest  Login
Title:

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

Document type:
Journal Article
Author(s):
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.
Journal title abbreviation:
Crit Care
Year:
2023
Journal volume:
27
Journal issue:
1
Fulltext / DOI:
doi:10.1186/s13054-023-04426-5
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/37237287
Print-ISSN:
1466-609X
TUM Institution:
Institut für Diagnostische und Interventionelle Radiologie (Prof. Makowski); Institut für KI und Informatik in der Medizin (Prof. Rückert); Klinik und Poliklinik für Innere Medizin II, Gastroenterologie (Prof. Schmid)
 BibTeX