Related Experiment Video
Updated: Nov 7, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication in Patients with COVID-19
Young Joon Fred Kwon1, Danielle Toussie1, Mark Finkelstein1
1Department of Diagnostic, Molecular, and Interventional Radiology (Y.J.F.K., D.T., M.F., M.A.C., S.Z.M., S.M., N.V., C.E., A.J., A.B., Y.S.G., M.S.C., Z.A.F.), Department of Neurosurgery (Y.J.F.K., E.K.O., A.B.C.), Sinai BioDesign (Y.J.F.K., A.B.C.), BioMedical Engineering and Imaging Institute (Z.A.F.), Mount Sinai COVID Informatics Center (Z.A.F., B.S.G.), and The Hasso Plattner Institute for Digital Health at Mount Sinai (B.S.G.), Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, Box 1136, New York, NY 10029-6574.
Insights
Deep learning models using chest X-rays and clinical data accurately predict coronavirus disease 2019 (COVID-19) severity and patient outcomes, including admission and intubation.
Area of Science:
- Artificial Intelligence in Medicine
- Radiology and Medical Imaging
- Infectious Disease Epidemiology
Background:
- Coronavirus disease 2019 (COVID-19) poses a significant global health challenge.
- Accurate prediction of disease severity and clinical outcomes is crucial for patient management.
- Chest radiography is a common imaging modality for COVID-19 assessment.
Purpose of the Study:
- To develop and train a deep learning classification algorithm.
- To predict chest radiograph severity scores in COVID-19 patients.
- To forecast clinical outcomes such as admission, intubation, and survival.
Main Methods:
- Retrospective cohort study of 338 COVID-19 patients from an urban health system.
- Chest radiographs were assessed for severity scores by expert radiologists.
- A deep learning algorithm was trained to predict outcomes using imaging and clinical data.
Main Results:
- The deep learning model achieved an AUC of 0.80 for predicting chest radiograph severity score.
- Models incorporating both imaging and clinical data showed improved prediction for intubation (AUC 0.88) and death (AUC 0.82).
- Clinical variable models showed moderate predictive power for intubation and death.
Conclusions:
- Combining chest radiography findings with clinical information significantly enhances the prediction of patient outcomes in COVID-19.
- Deep learning approaches show promise in improving prognostic accuracy for infectious respiratory diseases.
- This study highlights the synergistic value of multimodal data in predicting COVID-19 severity.
Purpose:
To train a deep learning classification algorithm to predict chest radiograph severity scores and clinical outcomes in patients with coronavirus disease 2019 (COVID-19).
Materials And Methods:
In this retrospective cohort study, patients aged 21-50 years who presented to the emergency department (ED) of a multicenter urban health system from March 10 to 26, 2020, with COVID-19 confirmation at real-time reverse-transcription polymerase chain reaction screening were identified. The initial chest radiographs, clinical variables, and outcomes, including admission, intubation, and survival, were collected within 30 days (n = 338; median age, 39 years; 210 men). Two fellowship-trained cardiothoracic radiologists examined chest radiographs for opacities and assigned a clinically validated severity score. A deep learning algorithm was trained to predict outcomes on a holdout test set composed of patients with confirmed COVID-19 who presented between March 27 and 29, 2020 (n = 161; median age, 60 years; 98 men) for both younger (age range, 21-50 years; n = 51) and older (age >50 years, n = 110) populations. Bootstrapping was used to compute CIs.
Results:
The model trained on the chest radiograph severity score produced the following areas under the receiver operating characteristic curves (AUCs): 0.80 (95% CI: 0.73, 0.88) for the chest radiograph severity score, 0.76 (95% CI: 0.68, 0.84) for admission, 0.66 (95% CI: 0.56, 0.75) for intubation, and 0.59 (95% CI: 0.49, 0.69) for death. The model trained on clinical variables produced an AUC of 0.64 (95% CI: 0.55, 0.73) for intubation and an AUC of 0.59 (95% CI: 0.50, 0.68) for death. Combining chest radiography and clinical variables increased the AUC of intubation and death to 0.88 (95% CI: 0.79, 0.96) and 0.82 (95% CI: 0.72, 0.91), respectively.
Conclusion:
The combination of imaging and clinical information improves outcome predictions.Supplemental material is available for this article.© RSNA, 2020.
More Related Videos
Related Concept Videos
01:29Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care
01:13Radiological Investigation III: Pulmonary Angiogram and PET Scan
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
01:30Radiological Investigation II: MRI and Ventilation Perfusion Scan
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
01:30Radiological Investigation I: X-ray and CT
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:20Imaging Studies for Cardiovascular System III: X-Ray
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...