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A DEEP LEARNING MODEL TO PREDICT THE NEED FOR MECHANICAL VENTILATION USING CHEST X-RAY IMAGES IN HOSPITALIZED COVID-19 PATIENTS (preprint)
medrxiv; 2020.
Preprint
in English
| medRxiv | ID: ppzbmed-10.1101.2020.08.17.20176917
ABSTRACT
PurposeEarly identification of a potentially deteriorating clinical course in hospitalized COVID-19 patients is critical since there exists a resource-demand gap for the ventilators. MaterialsWe aimed to develop and validate a deep learning-based approach to predict the need for mechanical ventilation as early as at the time of initial radiographic evaluation. We exploited the well-established DenseNet121 deep learning architecture for this purpose on 663 X-ray images derived from 528 hospitalized COVID-19 patients. Two Pulmonary and Critical Care experts blindly and independently evaluated the same X-ray images for purpose of validation. ResultsWe found that our deep learning model predicted the need for ventilation with a high accuracy, sensitivity and specificity (90.06%, 86.34% and 84.38%, respectively). This prediction was done approximately three days ahead of the actual intubation event. Our model also outperformed two Pulmonary and Critical Care experts who evaluated the same X-ray images and provided an incremental accuracy of 7.24-13.25%. ConclusionOur deep learning model accurately predicted the need for mechanical ventilation early during hospitalization of COVID-19 patients. Until effective preventive or treatment measures become widely available for COVID-19 patients, prognostic stratification as provided by our model is likely to be highly valuable.
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Main subject:
COVID-19
Language:
English
Year:
2020
Document Type:
Preprint
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