ArticleAcademic radiology2021
Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning.
Article in Academic radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- 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
- Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation.Bioengineering (Basel, Switzerland) · 2024Article
- Prediction of oxygen supplementation by a deep-learning model integrating clinical parameters and chest CT images in COVID-19.Japanese journal of radiology · 2023Article
- 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
- Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.Academic radiology · 2023Article
- Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution.Journal of clinical and translational science · 2023Article
- Detecting Racial/Ethnic Health Disparities Using Deep Learning From Frontal Chest Radiography.Journal of the American College of Radiology : JACR · 2022Article
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Authors and funding
18 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RATIONALE AND
objectivesThe clinical prognosis of outpatients with coronavirus disease 2019 (COVID-19) remains difficult to predict, with outcomes including asymptomatic, hospitalization, intubation, and death. Here we determined the prognostic value of an outpatient chest radiograph, together with an ensemble of deep learning algorithms predicting comorbidities and airspace disease to identify patients at a higher risk of hospitalization from COVID-19 infection. MATERIALS AND
methodsThis retrospective study included outpatients with COVID-19 confirmed by reverse transcription-polymerase chain reaction testing who received an ambulatory chest radiography between March 17, 2020 and October 24, 2020. In this study, full admission was defined as hospitalization within 14 days of the COVID-19 test for > 2 days with supplemental oxygen. Univariate analysis and machine learning algorithms were used to evaluate the relationship between the deep learning model predictions and hospitalization for > 2 days.
resultsThe study included 413 patients, 222 men (54%), with a median age of 51 years (interquartile range, 39-62 years). Fifty-one patients (12.3%) required full admission. A boosted decision tree model produced the best prediction. Variables included patient age, frontal chest radiograph predictions of morbid obesity, congestive heart failure and cardiac arrhythmias, and radiographic opacity, with an internally validated area under the curve (AUC) of 0.837 (95% CI: 0.791-0.883) on a test cohort.
conclusionDeep learning analysis of single frontal chest radiographs was used to generate combined comorbidity and pneumonia scores that predict the need for supplemental oxygen and hospitalization for > 2 days in patients with COVID-19 infection with an AUC of 0.837 (95% confidence interval: 0.791-0.883). Comorbidity scoring may prove useful in other clinical scenarios.
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