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Related Experiment Video

Updated: Nov 2, 2025

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

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Published on: September 19, 2025

Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory

Ayis Pyrros¹, Adam Eugene Flanders², Jorge Mario Rodríguez-Fernández³

¹DuPage Medical Group, Radiology.

Academic Radiology

|June 17, 2021

Summary

Deep learning analysis of chest X-rays can predict COVID-19 hospitalization risk in outpatients. This approach identifies patients needing supplemental oxygen, improving clinical prognosis prediction for coronavirus disease 2019.

Keywords:

COVID-19 chest radiography convolutional neural networks deep learning multi-task learning

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Medicine
Infectious Diseases

Background:

Predicting clinical outcomes for outpatients with COVID-19 is challenging.
Outcomes range from asymptomatic infection to severe disease requiring hospitalization or death.

Purpose of the Study:

To evaluate the prognostic value of outpatient chest radiographs.
To utilize deep learning algorithms for predicting comorbidities and airspace disease.
To identify patients at higher risk of hospitalization due to COVID-19.

Main Methods:

Retrospective study of 413 outpatients with COVID-19 confirmed by PCR.
Ambulatory chest radiography analyzed using deep learning models.
Machine learning algorithms assessed prediction of hospitalization (>2 days with oxygen).

Main Results:

A boosted decision tree model achieved an AUC of 0.837 (95% CI: 0.791-0.883).
Key predictors included age, morbid obesity, heart failure, arrhythmias, and radiographic opacity.
12.3% of patients required hospitalization with supplemental oxygen.

Conclusions:

Deep learning on chest radiographs can predict hospitalization risk in COVID-19 outpatients.
Combined comorbidity and pneumonia scores are generated.
This method may aid in identifying patients needing supplemental oxygen and hospitalization.