Chest radiograph and AI may predict need for hospitalization, supplemental oxygen in COVID patients: Study
USA: Deep learning analysis of chest x-ray may predict the need for supplemental oxygen and hospitalization in COVID-19 patients, finds a recent study.The result, published in the journal Academic Radiology, suggests that further and validation and extension of this methodology is warranted.Researchers created an AI program that first identified comorbid conditions such as chronic...
USA: Deep learning analysis of chest x-ray may predict the need for supplemental oxygen and hospitalization in COVID-19 patients, finds a recent study.
The result, published in the journal Academic Radiology, suggests that further and validation and extension of this methodology is warranted.
Researchers created an AI program that first identified comorbid conditions such as chronic obstructive pulmonary disease (COPD) and cardiac arrhythmias on frontal chest x-rays of COVID-19 patients. The algorithm then predicted the likelihood of whether those patients would require full hospital admission and supplemental oxygen within 14 days.
In the study, Ayis Pyrros, DuPage Medical Group, Radiology, and colleagues aimed to determine 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.
The retrospective study included 413 outpatients with COVID-19 confirmed by reverse transcription-polymerase chain reaction testing who received an ambulatory chest radiography between 3/17/2020 and 10/24/2020. 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.
Key findings of the study include:
- 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 on a test cohort.
"Deep 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," wrote the authors. "Comorbidity scoring may prove useful in other clinical scenarios."
The study titled, "Predicting Prolonged Hospitalization and Supplemental Oxygenation in Patients with COVID-19 Infection from Ambulatory Chest Radiographs using Deep Learning," is published in the journal Academic Radiology.
Medha Baranwal joined Medical Dialogues as an Editor in 2018 for Speciality Medical Dialogues. She covers several medical specialties including Cardiac Sciences, Dentistry, Diabetes and Endo, Diagnostics, ENT, Gastroenterology, Neurosciences, and Radiology. She has completed her Bachelors in Biomedical Sciences from DU and then pursued Masters in Biotechnology from Amity University. She has a working experience of 5 years in the field of medical research writing, scientific writing, content writing, and content management. She can be contacted at firstname.lastname@example.org. Contact no. 011-43720751