ArticleRadiology. Artificial intelligence2021
Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication in Patients with COVID-19 from the Emergency Department.
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Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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32citing papers in PubMed, 1 pooled it
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Artificial intelligence-based model for COVID-19 prognosis incorporating chest radiographs and clinical data; a retrospective model development and validation study.The British journal of radiology · 2022Trial
- Comparison of chest X-ray radiography AI model to comorbidities for predicting intensive care unit admission for COVID-19.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- Semi-quantitative software evaluation of COVID-19 CT examinations-correlation with clinical parameters.Journal of thoracic disease · 2025Article
- Integrating VAI-Assisted Quantified CXRs and Multimodal Data to Assess the Risk of Mortality.Journal of imaging informatics in medicine · 2025Article
- Advancing Clinical Practice: The Potential of Multimodal Technology in Modern Medicine.Journal of clinical medicine · 2024Article
- A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.JMIR formative research · 2023Article
- Investigation of the best effective fold of data augmentation for training deep learning models for recognition of contiguity between mandibular third molar and inferior alveolar canal on panoramic radiographs.Clinical oral investigations · 2023Article
- DeepCOVID-Fuse: A Multi-Modality Deep Learning Model Fusing Chest X-rays and Clinical Variables to Predict COVID-19 Risk Levels.Bioengineering (Basel, Switzerland) · 2023Article
- Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs.Journal of digital imaging · 2023Article
- Current and Emerging Knowledge in COVID-19.Radiology · 2023Review
- Additional value of chest CT AI-based quantification of lung involvement in predicting death and ICU admission for COVID-19 patients.Research in diagnostic and interventional imaging · 2022Article
- An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.Academic radiology · 2022Article
- Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT.Scientific reports · 2022Article
- A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data.Scientific reports · 2022Article
- Diagnostic performance of CT lung severity score and quantitative chest CT for stratification of COVID-19 patients.La Radiologia medica · 2022Article
- Dynamic change of COVID-19 lung infection evaluated using co-registration of serial chest CT images.Frontiers in public health · 2022Article
- Article
- Multiplex Testing of the Effect of Statins on Disease Severity Risk in COVID-19 Cases.Methods in molecular biology (Clifton, N.J.) · 2022Article
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Authors and funding
16 authors.
Young Joon Fred KwonDepartment 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.ORCID 0000-0002-0893-9472
Danielle ToussieDepartment 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.ORCID 0000-0002-8947-7002
Mark FinkelsteinDepartment 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.ORCID 0000-0003-0576-4109
Mario A CedilloDepartment 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.ORCID 0000-0002-2014-4664
Samuel Z MaronDepartment 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.ORCID 0000-0002-9805-2268
Sayan MannaDepartment 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.ORCID 0000-0001-8649-2592
Nicholas VoutsinasDepartment 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.ORCID 0000-0003-1177-758X
Corey EberDepartment 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.ORCID 0000-0003-3290-420X
Adam JacobiDepartment 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.ORCID 0000-0002-9057-9129
Adam BernheimDepartment 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.
Yogesh Sean GuptaDepartment 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.
Michael S ChungDepartment 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.ORCID 0000-0002-0976-4088
Zahi A FayadDepartment 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.ORCID 0000-0002-3439-7347
Benjamin S GlicksbergDepartment 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.ORCID 0000-0003-4515-8090
Eric K OermannDepartment 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.ORCID 0000-0002-1876-5963
Anthony B CostaDepartment 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.ORCID 0000-0002-2202-6450
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo 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
methodsIn 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 (
resultsThe 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.
conclusionThe combination of imaging and clinical information improves outcome predictions.
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